An individual’s trunk, joints, and other body parts are detected using the human pose estimation method [26]. The pose estimation technique detects body parts using images from a video or an image detector; it describes the anatomic details [27]. These images are processed into an algorithm. Key skeletal points serve as coordinates, generated by using the pose estimation method. Human pose estimation is important; it predicts human posture, behaviour by gait recognition, character tracking, action, and behaviour recognition [28]. Similarly, the method: Part affinity field is another ground-breaking computer vision technique, able to detect multiple 2D people poses in the wild with high accuracy [29].

Pictorial structures mode [30-32] expresses spatial relationships within body parts labelled as kinematic-priors-based, tree-structured graphic models. This model couples connected limbs, thus make up the classic articulated pose estimation technique. These methods make mistakes such as counting image data twice; this happens because connections between variables of the tree-structured model did not capture correctly. These errors occur on high-quality limb images used in the pictorial structure model [33].

Hierarchical models [34, 35] signify how the parts relate at various sizes and scales in a hierarchical tree structure. Based on assumptions, the image structure easily detects, discri-minates the location of small hard-to-locate parts within the entire limb system.

Interactions introduce loops and augment the tree structure with edges; it captures: long-range, occlusion, symmetry, and incorporates into non-tree models [36, 37]. The inference is required in methods for learning and test time. As a result, spatial relationships’ provide fast, efficient inference, with a parametric form; but, a trade-off with other models occurs.

Sequential-prediction-framework-based approaches [38] use complicated relationships between variables: it learns from an implicit spatial model, trains an inference procedure to achieve a performance output [39, 40].

Recently, the articulated pose estimation method [41, 42] combined with a convolutional architecture gained popularity in the computer vision community. This method [43] uses convolutional architecture to carry out a regression of the Cartesian coordinates [44]. It regresses an image to a confidence map and opts in for graphical models using spatial probability priors' heuristic initialization or energy functions designed by hand. This removes outliers on the regressed confidence maps; it also uses a dedicated network model for a precise refinement [45, 46]. This input to the regressed confidence maps combined with convolutional networks does not require hand-designed priors, has great receptive domains for learning, and attains a high level of performance within the entire precision region. Furthermore, it should not be carefully initialized and needs a dedicated precision refinement. A network module with a large receptive field is used in the work for capturing implicit spatial models [47], considered joint training’s advantages; the model we reviewed is trained globally because of convolutions differentiable attributes [42].

A deep network with the features of being able to use error feedback for training is seen in the work of [48]. It also uses Cartesian representation, as seen in that it is incapable of preserving spatial improbability, and that reduces the high precision regime’s accuracy [49].

The task of articulated pose estimation using Convolu-tional Pose Machines (CPMs) has been carried out. CPM inherit pose machine architecture’s benefits [38], integrating learning and inference tightly, the learning of long-range dependencies between multi-part cues and image implicitly, and a modular sequential design. It combines these with the benefits convolutional architecture provides. CPMs also include advantages such as the capability of handling large training datasets efficiently, a differentiable architecture that makes joint training with backpropagation possible, and the ability to learn spatial and image context's feature repre-sentations directly from data. Series of convolutional networks, 2D maps for each part's location make up CPMs.

CPMs [50] are robust and have high accuracy in the detection of human pose estimations’ data-sets, such as the Leeds Sports Pose (LSP) data-set [51], Human Pose data-set [52], and Max-Planck-Institute Informatics (MPII). The time required to train CPMs are extensive, and its detection speed is low. This makes it difficult to apply real-time tasks. Based on human pose estimation's standard datasets, excellent detection outputs are found in the Stacked Hourglass [53] of a similar duration. The new modes using the enhanced Stacked Hourglass include the 2017 models such as Learning Feature [54], Self-Adversarial Training [55], and multi-context [56], and the 2018 excellent models’ further improved accuracy. Certain metrics contained in these models, increase the time required for training, thereby limiting model use. To date, the model does not have a satisfactory accuracy.

The ability to extract the low-level feature is enhanced, using the more convoluted network structures, and deeper network layers of the enhanced CPM model [57]; and afterward, apply a system to fine-tune it. The enhanced CPM is proven to include an excellent image detection effect and high image classification accuracy, and a good human pose estimation model for designing a new network and apply a system of fine-tuning to increase the human pose estimation’s efficiency.

Gait Energy Image (GEI) is a combination of gait with a new spatiotemporal method for force representation to mark human walking behaviour for individual recognition [58]. The findings show the efficiency of combining gait and GEI approach for individual recognition, and the competitiveness of its performance [58, 59]. The GEI approach is used for studying individual recognition. The researchers used various tech-niques, methods to present the GEI approach as biased attributed in their survey. It is clear from the findings of their research that the system’s performance in real-time improved; hence, its application in real-word is possible [60]. It further used automated approaches to combine psychological methods for improving accuracy quality to classify human gait-based genders. According to their research, compared to other parts of the body, the major body parts for the gender recognition process include the chest, back, hair, and head. Even though the application process contains several impediments because of the differences in how humans appear, they include change of shoes and clothes, or when they lift objects, the gait classification is possible in a controlled environment.

The classification of human behaviour using 2-Directional 2-dimensional principles component analysis ((2D) 2PCA) and 2G (2D) 2PCA) Enhanced Gait Energy Image (EGEI) is proposed in the work [61]. The outcomes of the experiment revealed the simplicity of the algorithm and its capacity for realizing a higher classification accuracy within a short period. The system uses gait classification based on the silhouette, recommends books to visitors according to their age or gender, and in real-time [62]. The Support Vector Machine (SVM) has 77.5% accuracy in classification [63]; it combines the Denoised Energy Image (DEI) and GEI approach in pre-processing to present gender recognition’s initial design, outcomes, the training, and extraction of feature from the walking movements experiment. This method may provide high real-time accuracy.

The method of integrating information from the multi-view gait at the feature level is proposed [64], and it increases the effectiveness of the performance for the gender classification based on multi-view gait. Gait for human recognition was conducted [65]. Gait image’s features that are founded on information theory sets are referred to as image feature information gait. Gait information features are information set theory-based gait image features that are described by this research team. The concept of the information set was applied on the frames in a gait cycle, and two elements referred to as Gait Information Image with Sigmoid Feature (GIISF) extracted and Gait Information Image with Energy Feature (GII-EF) to derive the proposed Gait Information Image (GII). The identification of the gait was made using Nearest Neighbour (NN) for the classification. The robust feature-level fusion of directional vectors such as forward and backward diagonal, vertical, and horizontal vectors are used by this research team [66] to study gender recognition. First, they construct for each image sequence: 1) Gait Energy Image (GEI), followed by 2) Gradient Gait Energy Image (GGEI), which is achieved using neighbourhood gradient computation [67]. After that, differences in all the four directions were utilized as discriminative gait features. Afterward, SVM used in the classification process, while the largest multi-view CASIA-B (Chinese Academy of Sciences) datasets were used to test the model. The investigators report that their study outcomes were beneficial.

According to the literature review, the current most universal gait-based approaches to gender classification include GEI and GII approaches. As a result, this research focuses on contrasting GII approaches with GEI approaches to present a gait-based gender classification in real-time [68]. The one with the highest accuracy is beneficial for future ongoing research studies.