Human identification using biometric techniques has become the most important issue in recent years [1]. Human identification techniques based on fingerprint and face detection are available. These techniques are used to identify humans based on their distinguishing characteristics. Every person has unique fingerprints and iris patterns that are used for identification [2]. Scientists are increasingly interested in human gait as a biometric approach [3,4]. In comparison to fingerprint and face recognition technologies, gait recognition has a more beneficial system. Automatic human verification and video surveillance are two important applications of gait recognition [5,6]. The HGR has recently developed a dynamic study zone in biometric applications and has received significant attention in Computer Vision (CV) research [7]. Gait is a common and ordinary behavior of all humans, but it is a very complex process because it works with the association from an examination standpoint. The human gait recognition process is divided into two approaches: model-based and model-free [8].

The model-based approach directs human movement based on prior knowledge [9], whereas the model-free approach generates sketches of the human body known as posture generation or skeletons [10]. The model-based approach analyses human behaviors based on joint movement and upper/lower body parts. The model-free approach, on the other hand, is simpler to implement and requires less computational time. Many computerized techniques are used in the literature to automate this application [11]. Computer vision researchers used methods based on both classical and deep learning techniques. In traditional techniques, recognition is accomplished through a series of steps such as data preprocessing, segmenting the region of interest (ROI), feature extraction, and classification. The authors used contrast enhancement techniques during the preprocessing step [12]. In the following step, several segmentation techniques are used to extract the ROI. This is followed by the features extraction step, which extracts texture, shape, and point features. These features are enhanced further by feature reduction techniques such as PCA, Entropy, and a few others [13]. In recent years, the introduction of deep learning into machine learning has demonstrated great success in a variety of applications, including biometrics [14], surveillance [15,16], and medicine [17,18]. A simple deep learning model did not necessitate preprocessing or the use of raw data. Several hidden layers are used to extract the features. Convolutional layers, ReLU layers, max-pooling, and batch normalization are examples of hidden layers. The features are combined in one dimension at fully connected layers classified as Softmax layers [19].

Mehmood et al. [20] presented a novel deep learning-based HGR framework. The presented method consisted of four significant steps: preprocessing of video frames, modification of pre-trained deep learning models, exploiting only the best features with a firefly algorithm, and finally, classification. In this work, the fusion is also used to improve the representation of extracted features. The experiment was carried out on three CASIA B dataset angles: 18, 36, and 54. The calculated accuracy for each angle was 94.3 percent, 93.8 percent, and 94.7 percent, respectively. Anusha et al. [21] used optimal binary patterns to implement the HGR. They considered the problem of view-invariant clothing and conditions. MLOOP is the name given to the extracted binary patterns. They used MLOOP to extract histogram and horizontal width features and then reduced the irrelevant ones using a reduction technique. Two datasets were used in the experimental process, and they performed admirably. Arshad et al. [22] presented a deep learning and best features selection approach for HGR with various view invariants and cofactors. Two pre-trained models were used in this method, which was modified for feature extraction. In the following step, parallel approach-based features are fused and improved further using fuzzy entropy and skewness-based formulation. The experiment was carried out on four available datasets and yielded accuracy rates of 99.8, 99.7, 93.3, and 92.2 percent, respectively. Sugandhi et al. [23] proposed a novel HGR method based on frame aggregation. This work presents two features: the first is designed using dynamic variations of human body parts, and the second is based on first-order statistics. The frames in the first feature are divided into block cycles. The features level fusion is used in the final and executed classification. The experimental process was carried out on the CASIA B dataset and resulted in improved accuracy.

Based on these studies, we consider the following challenges of this work: I change in human view angle; ii) change in a human wearing condition such as clothes, etc.; iii) change of human characteristics during walking styles such as slow walk, fast walk, etc.; iv) deep learning model requires a large amount of data to train a good model, but it is not always possible to obtain data due to various factors. We proposed a new deep learning and Improved Ant Colony Optimization framework for accurate HGR to address these issues.

In terms of fully connected layers, modified two pre-trained models, VGG16 and ResNet101, and added a new layer with the connection of the preceding layers.

This section describes the proposed human gait recognition method. Fig. 1 depicts the main flow diagram of the proposed approach. Preprocessing datasets, feature extraction using pre-trained models, feature optimization, and classification are the main steps in this method. Deep transfer learning is used to modify two pre-trained models, Resnet 101 and Inception V3. The features are then extracted from both modified models. We get two resultant vectors as a result, which are then optimized using improved ant colony optimization (IACO). Finally, the final features are classified using multiclass classification methods.

The experimental process such as experimental setup, dataset, evaluation measures, and results is discussed in this section. The CASIA B dataset is utilized in this work and divide into 70:30. Its means that 70% dataset is used for the training purpose and the remaining 30% data for testing. During the training process, we initialized epoch's 100, iterations 300; mini-batch size is 64 and learning rate 0.0001. For learning, the Stochastic Gradient Descent (SGD) optimizer is employed. For the cross-validation, the ten-fold process was conducted. Multiple classifiers are used, and each classifier is validated by six measures such as recall rate, precision, accuracy, and name a few more. All the simulation of this work is conducted in MATLAB 2020a. The system used for this work is Corei7 with 16GB of RAM and 8 GB graphics card.

First, a brief discussion of the results section has been added to analyze the proposed framework. The results show that the proposed framework performed well on the chosen dataset. The accuracy of 0 and 180 degrees is better for modified ResNet101 and IACO, while the accuracy of 18 degrees is better for improved inceptionV3 and IACO. When compared to inceptionV3, the computational cost of improved ResNet101 and IACO is lower. Furthermore, the original computational cost of modified ResNet101 and InceptionV3 is nearly three times that of the proposed framework (applying after the IACO). Tab. 7 also includes a fair comparison with the most recent techniques. In this table, it is demonstrated that the proposed accuracy outperforms the existing techniques. Based on the results, we can conclude that the IACO aids in improving recognition accuracy while also reducing computational time. Because the accuracy of improved deep models is insufficient and falls short of recent techniques, we proposed an IACO algorithm. The choice of a deep model is the main limitation of this work because we consider both models instead of just one. In future studies, we will take this challenge into account and optimize any single deep model for HGR.