Research article Special Issues

Facial feature point recognition method for human motion image using GNN

  • academic editor:Weizheng Wang
  • Received: 29 September 2021 Revised: 14 January 2022 Accepted: 24 January 2022 Published: 10 February 2022
  • To address the problems of facial feature point recognition clarity and recognition efficiency in different human motion conditions, a facial feature point recognition method using Genetic Neural Network (GNN) algorithm was proposed. As the technical platform, weoll be using the Hikey960 development board. The optimized BP neural network algorithm is used to collect and classify human motion facial images, and the genetic algorithm is introduced into neural network algorithm to train human motion facial images. Combined with the improved GNN algorithm, the facial feature points are detected by the dynamic transplantation of facial feature points, and the detected facial feature points are transferred to the face alignment algorithm to realize facial feature point recognition. The results show that the efficiency and accuracy of facial feature point recognition in different human motion images are higher than 85% and the performance of anti-noise is good, the average recall rate is about 90% and the time-consuming is short. It shows that the proposed method has a certain reference value in the field of human motion image recognition.

    Citation: Qingwei Wang, Xiaolong Zhang, Xiaofeng Li. Facial feature point recognition method for human motion image using GNN[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3803-3819. doi: 10.3934/mbe.2022175

    Related Papers:

    [1] Waqar Afzal, Khurram Shabbir, Thongchai Botmart, Savin Treanţă . Some new estimates of well known inequalities for (h1,h2)-Godunova-Levin functions by means of center-radius order relation. AIMS Mathematics, 2023, 8(2): 3101-3119. doi: 10.3934/math.2023160
    [2] Waqar Afzal, Sayed M. Eldin, Waqas Nazeer, Ahmed M. Galal . Some integral inequalities for harmonical cr-h-Godunova-Levin stochastic processes. AIMS Mathematics, 2023, 8(6): 13473-13491. doi: 10.3934/math.2023683
    [3] Waqar Afzal, Khurram Shabbir, Thongchai Botmart . Generalized version of Jensen and Hermite-Hadamard inequalities for interval-valued (h1,h2)-Godunova-Levin functions. AIMS Mathematics, 2022, 7(10): 19372-19387. doi: 10.3934/math.20221064
    [4] Waqar Afzal, Khurram Shabbir, Savin Treanţă, Kamsing Nonlaopon . Jensen and Hermite-Hadamard type inclusions for harmonical h-Godunova-Levin functions. AIMS Mathematics, 2023, 8(2): 3303-3321. doi: 10.3934/math.2023170
    [5] Waqar Afzal, Najla M. Aloraini, Mujahid Abbas, Jong-Suk Ro, Abdullah A. Zaagan . Some novel Kulisch-Miranker type inclusions for a generalized class of Godunova-Levin stochastic processes. AIMS Mathematics, 2024, 9(2): 5122-5146. doi: 10.3934/math.2024249
    [6] Waqar Afzal, Thongchai Botmart . Some novel estimates of Jensen and Hermite-Hadamard inequalities for h-Godunova-Levin stochastic processes. AIMS Mathematics, 2023, 8(3): 7277-7291. doi: 10.3934/math.2023366
    [7] Iqra Nayab, Shahid Mubeen, Rana Safdar Ali, Faisal Zahoor, Muath Awadalla, Abd Elmotaleb A. M. A. Elamin . Novel fractional inequalities measured by Prabhakar fuzzy fractional operators pertaining to fuzzy convexities and preinvexities. AIMS Mathematics, 2024, 9(7): 17696-17715. doi: 10.3934/math.2024860
    [8] Sabila Ali, Rana Safdar Ali, Miguel Vivas-Cortez, Shahid Mubeen, Gauhar Rahman, Kottakkaran Sooppy Nisar . Some fractional integral inequalities via h-Godunova-Levin preinvex function. AIMS Mathematics, 2022, 7(8): 13832-13844. doi: 10.3934/math.2022763
    [9] Muhammad Bilal Khan, Muhammad Aslam Noor, Thabet Abdeljawad, Bahaaeldin Abdalla, Ali Althobaiti . Some fuzzy-interval integral inequalities for harmonically convex fuzzy-interval-valued functions. AIMS Mathematics, 2022, 7(1): 349-370. doi: 10.3934/math.2022024
    [10] Mujahid Abbas, Waqar Afzal, Thongchai Botmart, Ahmed M. Galal . Jensen, Ostrowski and Hermite-Hadamard type inequalities for h-convex stochastic processes by means of center-radius order relation. AIMS Mathematics, 2023, 8(7): 16013-16030. doi: 10.3934/math.2023817
  • To address the problems of facial feature point recognition clarity and recognition efficiency in different human motion conditions, a facial feature point recognition method using Genetic Neural Network (GNN) algorithm was proposed. As the technical platform, weoll be using the Hikey960 development board. The optimized BP neural network algorithm is used to collect and classify human motion facial images, and the genetic algorithm is introduced into neural network algorithm to train human motion facial images. Combined with the improved GNN algorithm, the facial feature points are detected by the dynamic transplantation of facial feature points, and the detected facial feature points are transferred to the face alignment algorithm to realize facial feature point recognition. The results show that the efficiency and accuracy of facial feature point recognition in different human motion images are higher than 85% and the performance of anti-noise is good, the average recall rate is about 90% and the time-consuming is short. It shows that the proposed method has a certain reference value in the field of human motion image recognition.



    The interval analysis discipline addresses uncertainty using interval variables in contrast to variables in the form of points, the calculation results are reported as intervals, preventing mistakes that may lead to false conclusions. Despite its long history, Moore [1], used interval analysis for the first time in 1969 to analyze automatic error reports. This led to an improvement in calculation performance, which attracted many scholars' attention. Due to their ability to be expressed as uncertain variables, intervals are commonly used in uncertain problems, such as computer graphics [2], decision-making analysis [3], multi-objective optimization [4], and error analysis [5]. Consequently, interval analysis has produced numerous excellent results, and interested readers can consult. [6,7,8].

    Meanwhile, numerous disciplines, including economics, control theory, and optimization, use convex analysis and many scholars have studied it, see [9,10,11,12]. Recently, generalized convexity of interval-valued functions (IVFS) has received extensive research and has been utilized in a large number of fields and applications, see [13,14,15,16]. The (A, s)-convex and (A, s)-concave mappings describe the continuity of IVFS, as described by Breckner in [17]. Numerous inequalities have recently been established for IVFS. By applying the generalized Hukuhara derivative to IVFS, Chalco-Cano et al. [18] derived some Ostrowski-type inclusions. Costa [19], established Opial type inequalities for the generalized Hukuhara differentiable IVFS. In general, we can define a classical Hermite Hadamard inequality as follows:

    η(t+u2)1ututη(ν)dνη(t)+η(u)2. (1.1)

    Considering this inequality was the first geometrical interpretation of convex mappings in elementary mathematics, it has gained a lot of attention. The following are some variations and generalizations of this inequality, see [20,21,22,23]. Initially in 2007, Varoşanec [24] developed the notion of h-convex. Several authors have contributed to the development of inequalities based on H.H using h-convex functions, see [25,26,27,28]. The harmonically h-convex functions introduced by Noor [29], are important generalizations of convex functions. Here are some recent results relating to harmonically h-convexity, see [30,31,32,33,34,35]. At present, these results are derived from inclusion relations and interval LU-order relationships, both of which have significant flaws because these are partial order relations. It can be demonstrated the validity of the claim by comparing examples from the literature with those derived from these old relations. In light of this, determining how to use a total order relation to investigate convexity and inequality is crucial. As an additional observation, the interval differences between endpoints are much closer in examples than in these old partial order relations. Because of this, the ability to analyze convexity and inequalities using a total order relation is essential. Therefore, we will focus our entire paper on Bhunia et al. [36], (CR)- order relation. Using cr-order, Rahman [37], studied nonlinear constrained optimization problems with cr-convex functions. Based on the notions of cr-order relation, Wei Liu and his co-authors developed a modified version of H.H and Jensen-type inequalities for h-convex and harmonic h-convex functions by using center radius order relation, see [38,39].

    Theorem 1.1 (See [38]). Let η:[t,u]RI+. Consider h:(0,1)R+ and h(12)0. If ηSHX(cr-h,[t,u],RI+) and η IR[t,u], then

    12h(12)η(2tut+u)crutututη(ν)ν2dνcr[η(t)+η(u)]10h(x)dx. (1.2)

    In addition, a Jensen-type inequality was also proved with harmonic cr-h-convexity.

    Theorem 1.2 (See [38]). Let diR+, zi[t,u], η:[t,u]RI+. If h is super multiplicative and non-negative function and ηSHX(cr-h,[t,u],RI+). Then the inequality become as:

    η(11Dkki=1dizi)crki=1h(diDk)η(zi). (1.3)

    Using the h-GL function, Ohud Almutairi and Adem Kiliman have proven the following result in 2019, see [40].

    Theorem 1.3. Let η:[t,u]R. If η is h-Godunova-Levin function and h(12)0. Then

    h(12)2η(t+u2)1ututη(ν)dν[η(t)+η(u)]10dxh(x). (1.4)

    This study is unique in that it introduces a notion of interval-valued harmonical h-Godunova-Levin functions that are related to a total order relation, called Center-Radius order, which is novel in the literature. By incorporating cr-interval-valued functions into inequalities, this article opens up a new avenue of research in inequalities. In contrast to classical interval-valued analysis, cr-order interval-valued analysis follows a different methodology. Based on the concept of center and radius, we calculate intervals as follows: tc=t_+¯t2 and tr=¯tt_2, respectively, where ¯t and t_ are endpoints of interval t.

    Inspired by. [15,34,38,39,41], This study introduces a novel class of harmonically cr-h-GL functions based on cr-order. First, we derived some H.H inequalities, then we developed the Jensen inequality using this new class. In addition, the study presents useful examples in support of its conclusions.

    Lastly the paper is designed as follows: In section 2, preliminary information is provided. The key problems are described in section 3. There is a conclusion at the end of section 6.

    Some notions are used in this paper that aren't defined in this paper, see [38,41]. The collection of intervals is denoted by RI of R, while the collection of all positive intervals can be denoted by R+I. For νR, the scalar multiplication and addition are defined as

    t+u=[t_,¯t]+[u_,¯u]=[t_+u_,¯t+¯u]
    νt=ν.[t_,¯t]={[νt_,μ¯t],ifν>0,{0},ifν=0,[ν¯t,νt_],ifν<0,

    respectively. Let t=[t_,¯t]RI, tc=t_+¯t2 is called center of interval t and tr=¯tt_2 is said to be radius of interval t. In the case of interval t, this is the (CR) form

    t=(t_+¯t2,¯tt_2)=(tc,tr).

    An order relation between radius and center can be defined as follows.

    Definition 2.1. (See [25]). Consider t=[t_,¯t]=(tc,tr), u=[u_,¯u]=(uc,ur)RI, then centre-radius order (In short cr-order) relation is defined as

    tcru{tc<uc,tcuc,tcuc,tc=uc.

    Further, we represented the concept of Riemann integrable (in short IR) in the context of IVFS [39].

    Theorem 2.1 (See [39]). Let φ:[t,u]RI be IVF given by η(ν)=[η_(ν),¯η(ν)] for each ν[t,u] and η_,¯η are IR over interval [t,u]. In that case, we would call η is IR over interval [t,u], and

    utη(ν)dν=[utη_(ν)dν,ut¯η(ν)dν].

    All Riemann integrables (IR) IVFS over the interval should be assigned IR[t,u].

    Theorem 2.2 (See [39]). Let η,ζ:[t,u]R+I given by η=[η_,¯η], and ζ=[ζ_,¯ζ]. If η,ζIR[t,u], and η(ν)crζ(ν) ν[t,u], then

    utη(ν)dνcrutζνdν.

    See interval analysis notations for a more detailed explanation, see [38,39].

    Definition 2.2 (See [39]). Consider h:[0,1]R+. We say that η:[t,u]R+ is known harmonically h-convex function, or that ηSHX(h,[t,u],R+), if t1,u1[t,u] and ν[0,1], we have

    η(t1u1νt1+(1ν)u1) h(ν)η(t1)+h(1ν)η(u1). (2.1)

    If in (2.1) replaced with it is called harmonically h-concave function or ηSHV(h,[t,u],R+).

    Definition 2.3. (See [27]). Consider h:(0,1)R+. We say that η:[t,u]R+ is known as harmonically h-GL function, or that ηSGHX(h,[t,u],R+), if t1,u1[t,u] and ν(0,1), we have

    η(t1u1νt1+(1ν)u1)η(t1)h(ν)+η(u1)h(1ν). (2.2)

    If in (2.2) replaced with it is called harmonically h-Godunova-Levin concave function or ηSGHV(h,[t,u],R+).

    Now let's look at the IVF concept with respect to cr-h-convexity.

    Definition 2.4 (See [39]) Consider h:[0,1]R+. We say that η=[η_,¯η]:[t,u]R+I is called harmonically cr-h-convex function, or that ηSHX(cr-h,[t,u],R+I), if t1,u1[t,u] and ν[0,1], we have

    η(t1u1νt1+(1ν)u1)cr h(ν)η(t1)+h(1ν)η(u1). (2.3)

    If in (2.3) cr replaced with cr it is called harmonically cr-h-concave function or ηSHV(cr-h,[t,u],R+I).

    Definition 2.5. (See [39]) Consider h:(0,1)R+. We say that η=[η_,¯η]:[t,u]R+I is called harmonically cr-h-Godunova-Levin convex function, or that ηSGHX(cr-h,[t,u],R+I), if t1,u1[t,u] and ν(0,1), we have

    η(t1u1νt1+(1ν)u1)crη(t1)h(ν)+η(u1)h(1ν). (2.4)

    If in (2.4) cr replaced with cr it is called harmonically cr-h-Godunova-Levin concave function or ηSGHV(cr-h,[t,u],R+I).

    Remark 2.1.

    (i) If h(ν)=1, in this case, Definition 2.5 becomes a harmonically cr-P-function [28].

    (ii) If h(ν)=1h(ν), in this case, Definition 2.5 becomes a harmonically cr h-convex function [28].

    (iii) If h(ν)=ν, in this case, Definition 2.5 becomes a harmonically cr-Godunova-Levin function [28].

    (iv) If h(ν)=1νs, in this case, Definition 2.5 becomes a harmonically cr-s-convex function [28].

    (v) If h(ν)=νs, in this case, Definition 2.5 becomes a harmonically cr-s-GL function [28].

    Proposition 3.1. Define h1,h2:(0,1)R+ functions that are non-negative and

    1h21h1,ν(0,1).

    If ηSGHX(cr-h2,[t,u],RI+), then ηSGHX(cr-h1,[t,u],RI+).

    Proof. Since ηSGHX(cr-h2,[t,u],RI+), then for all t1,u1[t,u],ν(0,1), we have

    η(t1u1νt1+(1ν)u1)crη(t1)h2(ν)+η(u1)h2(1ν)
    crη(t1)h1(ν)+η(u1)h1(1ν).

    Hence, ηSGHX(cr-h1,[t,u],RI+).

    Proposition 3.2. Let η:[t,u]RI given by [η_,¯η]=(ηc,ηr). If ηc and ηr are harmonically h-GL over [t,u], then η is known as harmonically cr-h-GL function over [t,u].

    Proof. Since ηc and ηr are harmonically h-GL over [t,u], then for each ν(0,1) and for all t1,u1[t,u], we have

    ηc(t1u1νt1+(1ν)u1)crηc(t1)h(ν)+ηc(u1)h(1ν),

    and

    ηr(t1u1νt1+(1ν)u1)crηr(t1)h(ν)+ηr(u1)h(1ν).

    Now, if

    ηc(t1u1νt1+(1ν)u1)ηc(t1)h(ν)+ηc(u1)h(1ν),

    then for each ν(0,1) and for all t1,u1[t,u],

    ηc(t1u1νt1+(1ν)u1)<ηc(t1)h(ν)+ηc(u1)h(1ν).

    Accordingly,

    ηc(t1u1νt1+(1ν)u1)crηc(t1)h(ν)+ηc(u1)h(1ν).

    Otherwise, for each ν(0,1) and for all t1,u1[t,u],

    ηr(t1u1νt1+(1ν)u1)ηr(t1)h(ν)+ηr(u1)h(1ν)η(t1u1νt1+(1ν)u1)crη(t1)h(ν)+η(u1)h(1ν).

    Based on all the above, and Definition 2.1, this can be expressed as follows:

    η(t1u1νt1+(1ν)u1)crη(t1)h(ν)+η(u1)h(1ν)

    for each ν(0,1) and for all t1,u1[t,u].

    This completes the proof.

    This section developed the H.H inequalities for harmonically cr-h-GL functions.

    Theorem 4.1. Consider h:(0,1)R+ and h(12)0. Let η:[t,u]RI+, if ηSGHX(cr-h,[t,u],RI+) and η IR[t,u], we have

    [h(12)]2f(2tut+u)crtuututη(ν)ν2dνcr[η(t)+η(u)]10dxh(x). (4.1)

    Proof. Since ηSGHX(cr-h,[t,u],RI+), we have

    h(12)η(2tut+u)crη(tuxt+(1x)u)+η((tu1x)t+xu).

    On integration over (0,1), we have

    h(12)η(2tut+u)cr[10η(tuxt+(1x)u)dx+10η(tu(1x)t+xu)dx]=[10η_(tuxt+(1x)u)dx+10η_(tu(1x)t+xu)dx,10¯η(tuxt+(1x)u)dx+10¯η(tu(1x)t+xu)dx]=[2tuututη_(ν)ν2dν,2tuutut¯η(ν)ν2dν]=2tuututη(ν)ν2dν. (4.2)

    By Definition 2.5, we have

    η(tuxt+(1x)u)crη(t)h(x)+η(u)h(1x).

    On integration over (0, 1), we have

    10η(tuxt+(1x)u)dxcrη(t)10dxh(x)+η(u)10dxh(1x).

    Accordingly,

    utututη(ν)ν2dνcr[η(t)+η(u)]10dxh(x). (4.3)

    Adding (4.2) and (4.3), results are obtained as expected

    h(12)2η(2tut+u)crutututη(ν)ν2dνcr[η(t)+η(u)]10dxh(x).

    Remark 4.1.

    (i) If h(x)=1, in this case, Theorem 4.1 becomes result for harmonically cr- P-function:

    12η(2tut+u)crutututη(ν)ν2dνcr[η(t)+η(u)].

    (ii) If h(x)=1x, in this case, Theorem 4.1 becomes result for harmonically cr-convex function:

    η(2tut+u)crutututη(ν)ν2dνcr[η(t)+η(u)]2.

    (iii) If h(x)=1(x)s, in this case, Theorem 4.1 becomes result for harmonically cr-s-convex function:

    2s1η(2tut+u)crutututη(ν)ν2dνcr[η(t)+η(u)]s+1.

    Example 4.1. Let [t,u]=[1,2], h(x)=1x, x (0,1). η:[t,u]RI+ is defined as

    η(ν)=[1ν4+2,1ν4+3],

    where

    h(12)2η(2tut+u)=η(43)=[431256,849256],
    utututη(ν)ν2dν=2[21(2ν41ν6)dν,21(3ν4+1ν6)dν]=[258160,542160],
    [η(t)+η(u)]10dxh(x)=[478,1138].

    As a result,

    [431256,849256]cr[258160,542160]cr[478,1138].

    Thus, proving the theorem above.

    Theorem 4.2. Consider h:(0,1)R+ and h(12)0. Let η:[t,u]RI+, if ηSGX(cr-h,[t,u],RI+) and η IR[t,u], we have

    [h(12)]24η(2tut+u)cr1cr1ututη(ν)ν2dνcr2
    cr{[η(t)+η(u)][12+1h(12)]}10dxh(x),

    where

    1=[h(12)]4[η(4tu3t+u)+η(4tu3u+t)],
    2=[η(2tut+u)+η(t)+η(u)2)]10dxh(x).

    Proof. Consider [t,t+u2], we have

    η(4tut+3u)crη(t2tut+uxt+(1x)2tut+u    )[h(12)]+η(t2tut+u(1x)t+x2tut+u    )[h(12)].

    Integration over (0,1), we have

    [h(12)]4η(4tuu+3t)crutut2tut+uuη(ν)ν2dν. (4.4)

    Similarly for interval [t+u2,u], we have

    [h(12)]4η(4tut+3u)crututt2tut+uη(ν)ν2dν. (4.5)

    Adding inequalities (4.4) and (4.5), we get

    1=[h(12)]4[η(4tuu+3t)+η(4utt+3u)]crutututη(ν)ν2dν.

    Now

    [h(12)]24η(2tut+u)=[h(12)]24η(12(4tu3u+t)+12(4tu3t+u))cr[h(12)]24[η(4tuu+3t)h(12)+η(4tu3u+t)h(12)]=[h(12)]4[η(4tuu+3t)+η(4tu3u+t)]=1crututtuη(ν)ν2dνcr12[η(t)+η(u)+2η(2tut+u)]10dxh(x)=2cr[η(t)+η(u)2+η(t)h(12)+η(u)h(12)]10dxh(x)cr[η(t)+η(u)2+1h(12)[η(t)+η(u)]]10dxh(x)cr{[η(t)+η(u)][12+1h(12)]}10dxh(x).

    Example 4.2. Thanks to example 4.1, we have

    [h(12)]24η(2tut+u)=η(43)=[431256,849256],
    1=12[η(85)+η(87)]=[66794096,138014096],
    2=[η(1)+η(2)2+η(43)]10dxh(x),
    2=[1935512,4465512],
    {[η(t)+η(u)][12+1h(12)]}10dxh(x)=[478,1138].

    Thus, we obtain

    [431256,849256]cr[66794096,138014096]cr[258160,542160]cr[1935512,4465512]cr[478,1138].

    This proves the above theorem.

    Theorem 4.3. Let η,ζ:[t,u]RI+,h1,h2:(0,1)R+ such that h1,h20. If ηSGHX(cr -h1,[t,u],RI+), ζSGHX(cr-h2,[t,u],RI+) and η,ζ IR[v,w] then, we have

    utututη(ν)ζ(ν)ν2dνcrM(t,u)101h1(x)h2(x)dx+N(t,u)101h1(x)h2(1x)dx, (4.6)

    where

    M(t,u)=η(t)ζ(t)+η(u)ζ(u),N(t,u)=η(t)ζ(u)+η(u)ζ(t).

    Proof. Conider ηSGHX(cr-h1,[t,u],RI+), ζSGHX(cr-h2,[t,u],RI+) then, we have

    η(tutx+(1x)u)crη(t)h1(x)+η(u)h1(1x),
    ζ(tutx+(1x)u)crζ(t)h2(x)+ζ(u)h2(1x).

    Then,

    η(tutx+(1x)u)ζ(tutx+(1x)u)
    crη(t)ζ(t)h1(x)h2(x)+η(t)ζ(u)h1(x)h2(1x)+η(u)ζ(t)h1(1x)h2(x)+η(u)ζ(u)h1(1x)h2(1x).

    Integration over (0, 1), we have

    10η(tutx+(1x)u)ζ(tutx+(1x)u)dx=[10η_(tutx+(1x)u)ζ_(tutx+(1x)u)dx,10¯η(tutx+(1x)u)¯ζ(tutx+(1x)u)dx]=[utututη_(ν)ζ_(ν)ν2dν,ututut¯η(ν)¯ζ(ν)ν2dν]=utututη(ν)ζ(ν)ν2dνcr10[η(t)ζ(t)+η(u)ζ(u)]h1(x)h2(x)dx+10[η(t)ζ(u)+η(u)ζ(t)]h1(x)h2(1x)dx.

    It follows that

    utututη(ν)ζ(ν)ν2dνcrM(t,u)101h1(x)h2(x)dx+N(t,u)101h1(x)h2(1x)dx.

    Theorem is proved.

    Example 4.3. Let [t,u]=[1,2], h1(x)=h2(x)=1x x (0,1). η,ζ:[t,u]RI+ be defined as

    η(ν)=[1ν4+2,1ν4+3],ζ(ν)=[1ν+1,1ν+2].

    Then,

    utututη(ν)ζ(ν)ν2dν=[282640,5986640],M(t,u)101h1(x)h2(x)dx=M(1,2)10x2dx=[3196,62996],N(t,u)101h1(x)h2(1x)dx=N(1,2)10(xx2)dx=[112,30796].

    It follows that

    [282640,5986640]cr[3196,62996]+[112,30796]=[1332,394].

    This proves the above theorem.

    Theorem 4.4. Let η,ζ:[t,u]RI+,h1,h2:(0,1)R+ such that h1,h20. If ηSGHX(cr-h1,[t,u],RI+), ζSGHX(cr-h2,[t,u],RI+) and η,ζ IR[v,w] then, we have

    h1(12)h2(12)2η(2tut+u)ζ(2tut+u)crutututη(ν)ζ(ν)ν2dμ+M(t,u)101h1(x)h2(1x)dx+N(t,u)101h1(x)h2(x)dx.

    Proof. Since ηSGHX(cr-h1,[t,u],RI+), ζSGHX(cr-h2,[t,u],RI+), we have

    η(2tut+u)crη(tutx+(1x)u)h1(12)+η(tut(1x)+xu)h1(12),ζ(2tut+u)crζ(tutx+(1x)u)h2(12)+ζ(tut(1x)+xu)h2(12).

    Then,

    η(2tut+u)ζ(2tut+u)cr1h1(12)h2(12)[η(tutx+(1x)u)ζ(tutx+(1x)u)+η(tut(1x)+xu)ζ(tut(1x)+xu)]+1h1(12)h2(12)[η(tutx+(1x)u)ζ(tut(1x)+xu)+η(tut(1x)+xu)ζ(tutx+(1x)u)]cr1h1(12)h2(12)[η(tutx+(1x)u)ζ(tutx+(1x)u)+η(tut(1x)+xu)ζ(tut(1x)+xu)]+1h1(12)h2(12)[(η(t)h1(x)+η(u)h1(1x))(ζ(u)h2(1x)+ζ(u)h2(x))+(η(t)h1(1x)+η(u)h1(x))(ζ(t)h2(x)+ζ(u)h2(1x))]cr1h1(12)h2(12)[η(tutx+(1x)u)ζ(tutx+(1x)u)+η(tut(1x)+ux)ζ(tut(1x)+ux)]+1h1(12)h2(12)[(1h1(x)h2(1x)+1h1(1x)h2(x))M(t,u)+(1h1(x)h2(x)+1h1(1x)h2(1x))N(t,u)].

    Integration over (0,1), we have

    10η(2tut+u)ζ(2tut+u)dx=[10η_(2tut+u)ζ_(2tut+u)dx,10¯η(2tut+u)¯ζ(2tut+u)dx]=η(2tut+u)ζ(2tut+u)dxcr2h1(12)h2(12)[utututη(ν)ζ(ν)ν2dν]+2h(12)h(12)[M(t,u)101h1(x)h2(1x)dx+N(t,u)101h1(x)h2(x)dx].

    Multiply both sides by h1(12)h2(12)2 above equation, we get required result

    h1(12)h2(12)2η(2tut+u)ζ(2tut+u)crutututη(ν)ζ(ν)ν2dμ+M(t,u)101h1(x)h2(1x)dx+N(t,u)101h1(x)h2(x)dx.

    Example 4.4. Let [t,u]=[1,2], h1(x)=h2(x)=1x, x (0,1). η,ζ:[t,u]RI+ be defined as

    η(ν)=[1ν4+2,1ν4+3],ζ(ν)=[1ν+1,1ν+2].

    Then,

    h1(12)h2(12)2η(2tut+u)ζ(2tut+u)=2η(43)ζ(43)=[431512,9339512],utututη(ν)ζ(ν)ν2dν=[282640,5986640],M(t,u)101h1(x)h2(1x)dx=M(1,2)10(xx2)dx=[31192,629192],N(t,u)101h1(x)h2(x)dx=N(1,2)10x2dx=[16,30748].

    It follows that

    [431512,9339512]cr[282640,5986640]+[31192,629192]+[16,30748]=[123160,76140].

    This proves the above theorem.

    Theorem 5.1. Let diR+, zi[t,u]. If h is non-negative and super multiplicative function or ηSGHX(cr-h,[t,u],RI+). Then the inequality become as :

    η(11Dkki=1dizi)crki=1[η(zi)h(diDk)], (5.1)

    where Dk=ki=1di.

    Proof. If k=2, inequality (5.1) holds. Assume that inequality (5.1) also holds for k1, then

    η(11Dkki=1dizi)=η(1dkDkzk+k1i=1diDkzi)=η(1dkDkzk+Dk1Dkk1i=1diDk1zi)crη(zk)h(dkDk)+η(k1i=1diDk1zi)h(Dk1Dk)crη(zk)h(dkDk)+k1i=1[η(zi)h(diDk1)]1h(Dk1Dk)crη(zk)h(dkDk)+k1i=1[η(zi)h(diDk)]crki=1[η(zi)h(diDk)].

    Therefore, the result can be proved by mathematical induction.

    Remark 5.1.

    (i) If h(x)=1, in this case, Theorem 5.1 becomes result for harmonically cr- P-function:

    η(11Dkki=1dizi)crki=1η(zi).

    (ii) If h(x)=1x, in this case, Theorem 5.1 becomes result for harmonically cr-convex function:

    η(11Dkki=1dizi)crki=1diDkη(zi).

    (iii) If h(x)=1(x)s, in this case, Theorem 5.1 becomes result for harmonically cr-s-convex function:

    η(11Dkki=1dizi)crki=1(diDk)sη(zi).

    This study presents a harmonically cr-h-GL concept for IVFS. Using this new concept, we study Jensen and H.H inequalities for IVFS. This study generalizes results developed by Wei Liu [38,39] and Ohud Almutairi [34]. Several relevant examples are provided as further support for our basic conclusions. It might be interesting to determine equivalent inequalities for different types of convexity in the future. Under the influence of this concept, a new direction begins to emerge in convex optimization theory. Using the cr-order relation, we will study automatic error analysis with intervals and apply harmonically cr-h-GL functions to optimize problems. Using this concept, we aim to benefit and advance the research of other scientists in various scientific disciplines.

    This research received funding support from the NSRF via the Program Management Unit for Human Resources and Institutional Development, Research and Innovation (Grant number B05F650018).

    The authors declare that there is no conflicts of interest in publishing this paper.



    [1] Z. Xu, B. Li, M. Geng, Y. Yuan, AnchorFace: An anchor-based facial landmark detector across large poses, preprint, arXiv: 2007.03221.
    [2] P. Gao, K. Lu, J. Xue, J. Lyu, L. Shao, A facial landmark detection method based on deep knowledge transfer, IEEE Trans. Neural Networks Learn. Syst., 2021. https://doi.org/10.1109/TNNLS.2021.3105247 doi: 10.1109/TNNLS.2021.3105247
    [3] B. Guo, F. Da, Expression-invariant 3D face recognition based on local descriptors, J. Comput. - Aided Des. Comput. Graphics, 31 (2019), 1086–1094. https://doi.org/10.3724/SP.J.1089.2019.17433 doi: 10.3724/SP.J.1089.2019.17433
    [4] D. Wu, X. Jing, L. Zhang, W. Wang, Face recognition with Gabor feature based on Laplacian Pyramid, J. Comput. Appl., z2 (2017), 63–66.
    [5] Y. Guo, E. She, Q. Wang, Z. Li, Face point cloud registration based on improved surf algorithm, Opt. Technol., 44 (2018), 333–338. https://doi.org/10.13741/j.cnki.11-1879/o4.2018.03.014 doi: 10.13741/j.cnki.11-1879/o4.2018.03.014
    [6] J. Xu, Z. Wu, Y. Xu, J. Zeng, Face recognition based on PCA, LDA and SVM, Comput. Eng. Appl., 55 (2019), 34–37. https://doi.org/10.3778/j.issn.1002-8331.1903-0286 doi: 10.3778/j.issn.1002-8331.1903-0286
    [7] T. Liu, X. Zhou, X. Yan, LDA facial expression recognition algorithm combining optical flow characteristics with Gaussian, Comput. Sci., 45 (2018), 286–290.
    [8] L. Sun, C. Zhao, Z. Yan, P. Liu, T. Duckett, R. Stolkin, A novel weakly-supervised approach for RGB-D-based nuclear waste object detection, IEEE Sens. J., 19 (2019), 3487–3500. https://doi.org/10.1109/JSEN.2018.2888815 doi: 10.1109/JSEN.2018.2888815
    [9] P. Liu, H. Yu, S. Cang, Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances, Nonlinear Dyn., 98 (2019), 1447–1464. https://doi.org/10.1007/s11071-019-05170-8 doi: 10.1007/s11071-019-05170-8
    [10] Z. Tang, H. Yu, C. Lu, P. Liu, X. Jin, Single-trial classification of different movements on one arm based on ERD/ERS and corticomuscular coherence, IEEE Access, 7 (2019), 128185–128197. https://doi.org/10.1109/ACCESS.2019.2940034 doi: 10.1109/ACCESS.2019.2940034
    [11] Z. Tang, C. Li, J. Wu, P. Liu, S. Cheng, Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI, Front. Inf. Technol. Electronic Eng., 20 (2019), 1087–1098. https://doi.org/10.1631/FITEE.1800083 doi: 10.1631/FITEE.1800083
    [12] H. Xiong, C. Jin, M. Alazab, K. Yeh, H. Wang, T. R. R. Gadekallu, et al., On the design of blockchain-based ECDSA with fault-tolerant batch verication protocol for blockchain-enabled IoMT, IEEE J. Biomed. Health Inf., 2021. https://doi.org/10.1109/JBHI.2021.3112693 doi: 10.1109/JBHI.2021.3112693
    [13] W. Wang, C. Qiu, Z. Yin, G. Srivastava, T. R. R. Gadekallu, F. Alsolami, et al., Blockchain and PUF-based lightweight authentication protocol for wireless medical sensor networks, IEEE Internet Things J., 2021. https://doi.org/10.1109/JIOT.2021.3117762 doi: 10.1109/JIOT.2021.3117762
    [14] Z. Xia, J. Xing, C. Wang, X. Li, Gesture recognition algorithm of human motion target based on deep neural network, Mobile Inf. Syst., 2021 (2021), 1–12. https://doi.org/10.1155/2021/2621691 doi: 10.1155/2021/2621691
    [15] G. Sang, Y. Chao, R. Zhu, Expression-insensitive three-dimensional face recognition algorithm based on multi-region fusion, J. Comput. Appl., 39 (2019), 1685–1689. https://doi.org/10.11772/j.issn.1001-9081.2018112301 doi: 10.11772/j.issn.1001-9081.2018112301
    [16] X. Zhou, J. Zhou, R. Xu, New algorithm for face recognition based on the combination of multi-sample conventional collaborative and inverse linear regression, J. Electron. Meas. Instrum., 32 (2018), 96–101. https://doi.org/10.13382/j.jemi.2018.06.014 doi: 10.13382/j.jemi.2018.06.014
    [17] F. Wang, Y. Zhang, D. Zhang, H. Shao, C. Cheng, Research on application of convolutional neural networks in face recognition based on shortcut connection, J. Electron. Meas. Instrum., 32 (2018), 80–86. https://doi.org/10.13382/j.jemi.2018.04.012 doi: 10.13382/j.jemi.2018.04.012
    [18] X. Ma, X. Li, Dynamic gesture contour feature extraction method using residual network transfer learning, Wireless Commun. Mobile Comput., 2021 (2021). https://doi.org/10.1155/2021/1503325 doi: 10.1155/2021/1503325
    [19] Y. Kim, K. Lee, A novel approach to predict ingress/egress discomfort based on human motion and biomechanical analysis, Appl. Ergon., 75 (2019), 263–271. https://doi.org/10.1016/j.apergo.2018.11.003 doi: 10.1016/j.apergo.2018.11.003
    [20] L. Wang, Z. Ding, Y. Fu, Low-rank transfer human motion segmentation, IEEE Trans. Image Process., 28 (2019), 1023–1034. https://doi.org/10.1109/TIP.2018.2870945 doi: 10.1109/TIP.2018.2870945
    [21] M. Kostinger, P. Wohlhart, P. M. Roth, H. Bischof, Annotated facial landmarks in the wild: A largescale, real-world database for facial landmark localization, in 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), (2011), 2144–2151. https://doi.org/10.1109/ICCVW.2011.6130513
    [22] W. Wu, C. Qian, S. Yang, Q. Wang, Y. Cai, Q. Zhou, Look at boundary: A boundary-aware face alignment algorithm, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 2129–2138. https://doi.org/10.1109/CVPR.2018.00227
  • This article has been cited by:

    1. Waqar Afzal, Khurram Shabbir, Savin Treanţă, Kamsing Nonlaopon, Jensen and Hermite-Hadamard type inclusions for harmonical h-Godunova-Levin functions, 2023, 8, 2473-6988, 3303, 10.3934/math.2023170
    2. Tareq Saeed, Waqar Afzal, Khurram Shabbir, Savin Treanţă, Manuel De la Sen, Some Novel Estimates of Hermite–Hadamard and Jensen Type Inequalities for (h1,h2)-Convex Functions Pertaining to Total Order Relation, 2022, 10, 2227-7390, 4777, 10.3390/math10244777
    3. Waqar Afzal, Thongchai Botmart, Some novel estimates of Jensen and Hermite-Hadamard inequalities for h-Godunova-Levin stochastic processes, 2023, 8, 2473-6988, 7277, 10.3934/math.2023366
    4. Waqar Afzal, Evgeniy Yu. Prosviryakov, Sheza M. El-Deeb, Yahya Almalki, Some New Estimates of Hermite–Hadamard, Ostrowski and Jensen-Type Inclusions for h-Convex Stochastic Process via Interval-Valued Functions, 2023, 15, 2073-8994, 831, 10.3390/sym15040831
    5. Waqar Afzal, Najla Aloraini, Mujahid Abbas, Jong-Suk Ro, Abdullah A. Zaagan, Some novel Kulisch-Miranker type inclusions for a generalized class of Godunova-Levin stochastic processes, 2024, 9, 2473-6988, 5122, 10.3934/math.2024249
    6. Ahsan Fareed Shah, Serap Özcan, Miguel Vivas-Cortez, Muhammad Shoaib Saleem, Artion Kashuri, Fractional Hermite–Hadamard–Mercer-Type Inequalities for Interval-Valued Convex Stochastic Processes with Center-Radius Order and Their Related Applications in Entropy and Information Theory, 2024, 8, 2504-3110, 408, 10.3390/fractalfract8070408
    7. Mujahid Abbas, Waqar Afzal, Thongchai Botmart, Ahmed M. Galal, Jensen, Ostrowski and Hermite-Hadamard type inequalities for h-convex stochastic processes by means of center-radius order relation, 2023, 8, 2473-6988, 16013, 10.3934/math.2023817
    8. Waqar Afzal, Mujahid Abbas, Waleed Hamali, Ali M. Mahnashi, M. De la Sen, Hermite–Hadamard-Type Inequalities via Caputo–Fabrizio Fractional Integral for h-Godunova–Levin and (h1, h2)-Convex Functions, 2023, 7, 2504-3110, 687, 10.3390/fractalfract7090687
    9. Vuk Stojiljković, Rajagopalan Ramaswamy, Ola A. Ashour Abdelnaby, Stojan Radenović, Some Refinements of the Tensorial Inequalities in Hilbert Spaces, 2023, 15, 2073-8994, 925, 10.3390/sym15040925
    10. Vuk Stojiljković, Nikola Mirkov, Stojan Radenović, Variations in the Tensorial Trapezoid Type Inequalities for Convex Functions of Self-Adjoint Operators in Hilbert Spaces, 2024, 16, 2073-8994, 121, 10.3390/sym16010121
    11. Waqar Afzal, Mujahid Abbas, Sayed M. Eldin, Zareen A. Khan, Some well known inequalities for (h1,h2)-convex stochastic process via interval set inclusion relation, 2023, 8, 2473-6988, 19913, 10.3934/math.20231015
    12. Waqar Afzal, Mujahid Abbas, Jongsuk Ro, Khalil Hadi Hakami, Hamad Zogan, An analysis of fractional integral calculus and inequalities by means of coordinated center-radius order relations, 2024, 9, 2473-6988, 31087, 10.3934/math.20241499
    13. Vuk Stojiljkovic, Generalized Tensorial Simpson type Inequalities for Convex functions of Selfadjoint Operators in Hilbert Space, 2024, 6, 2667-7660, 78, 10.47087/mjm.1452521
    14. Waqar Afzal, Daniel Breaz, Mujahid Abbas, Luminiţa-Ioana Cotîrlă, Zareen A. Khan, Eleonora Rapeanu, Hyers–Ulam Stability of 2D-Convex Mappings and Some Related New Hermite–Hadamard, Pachpatte, and Fejér Type Integral Inequalities Using Novel Fractional Integral Operators via Totally Interval-Order Relations with Open Problem, 2024, 12, 2227-7390, 1238, 10.3390/math12081238
    15. Waqar Afzal, Najla M. Aloraini, Mujahid Abbas, Jong-Suk Ro, Abdullah A. Zaagan, Hermite-Hadamard, Fejér and trapezoid type inequalities using Godunova-Levin Preinvex functions via Bhunia's order and with applications to quadrature formula and random variable, 2024, 21, 1551-0018, 3422, 10.3934/mbe.2024151
    16. Zareen A. Khan, Waqar Afzal, Mujahid Abbas, Jongsuk Ro, Najla M. Aloraini, A novel fractional approach to finding the upper bounds of Simpson and Hermite-Hadamard-type inequalities in tensorial Hilbert spaces by using differentiable convex mappings, 2024, 9, 2473-6988, 35151, 10.3934/math.20241671
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3639) PDF downloads(158) Cited by(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog