Research on the design of gait recognition algorithm in multi-view scene based on deep learning

View difference is a factor that degrades the accuracy of gait recognition. A solution to reducing accuracy degradation is applying a view transformation model (VTM) that encodes a joint subspace of multi-view gait features trained from multiple training subjects. In the VTM framework, once an intrinsic vector of a test subject in the joint subspace is estimated from a gait feature with a source view (e.g. probe view), a gait feature with a destination view (e.g. gallery view) is generated for the same-view matching. Although this family of methods can improve the total accuracy, the quality of generated gait features depends on a test gait feature, and may be relevant to the accuracy of gait recognition. We therefore propose a method of incorporating the quality measure of the generated gait feature into the VTM framework. We employ the projection error into the joint subspace as the quality measure. A posterior probability is then computed by incorporating the quality measure. The accuracy evaluation against a subset of a public database collected from 1,912 subjects shows that the proposed method further improves the accuracy.

Based on the current market prospects of wearable devices, gait recognition accuracy and real-time market demand, a fusion gait real-time recognition algorithm is designed. The paper introduces the method of limiting the original step signal by using the method of limiting filtering and moving smoothing filtering. At the same time, using dynamic threshold detection and similarity algorithm fusion method, using dynamic window method, the step data is sequentially subjected to dynamic threshold. Only if the two algorithms are simultaneously established, the decision step is established. The fusion gait recognition algorithm is designed to achieve real-time gait recognition. and finally obtain more accurate detection results.

Human gait recognition (HGR) has been employed as a biometric technique for security purposes over the last decade. Various factors, including clothing, carrying items, and walking surfaces, can influence the performance of gait recognition. Additionally, identifying individuals from different viewpoints presents a significant challenge in HGR. Numerous conventional and deep learning techniques have been introduced in the literature for HGR, but traditional methods are not well suited to handling large datasets. This research explores the effectiveness of four deep learning models for gait identification in the CASIA B dataset: the convolutional neural network (CNN), multi-layer perceptron (MLP), self-organizing map (SOMs), and transfer learning with EfficientNet. The selected deep learning techniques offer robust feature extraction and the efficient handling of large datasets, making them ideal in enhancing the accuracy of gait recognition. The collection includes gait sequences from 10 individuals, with a total of 92,596 images that have been reduced to 64 × 64 pixels for uniformity. A modified model was developed by integrating sequential convolutional layers for detailed spatial feature extraction, followed by dense layers for classification, optimized through rigorous hyperparameter tuning and regularization techniques, resulting in an accuracy of 97.12% for the test set. This work enhances our understanding of deep learning methods in gait analysis, offering significant insights for choosing optimal models in security and surveillance applications.

Face recognition is an established area of research in computer vision and it had played a great role in developing content based personal retrieval systems from real time surveillance video feeds. Face recognition in live videos is a complex problem as facial features fall into high dimensional space and involves large search time. Though, there is an extensive improvement in computational infrastructure over the years, the need for improved search algorithms without increase in cost is a challenge. Existingmethodologies in literature fail to perform in real time scenarios as the cost of feature matching is high. Hence, this research work proposes a Two-Bit Transform AccelerativeRegressive Frame Check algorithm (2BT-ARFCA) methodology that facilitates face recognition in video at a faster rate, suitable for surveillance and authentication applications. Finally the results are experimentally validated with variousvideo datasets and the state-of-the-art techniques proves that the proposed method performs better in terms of Specificity, Sensitivity, Mean Square Error (MSE), Peak signal to noise Ratio (PSNR), The Structural Similarity Index (SSIM) and accuracy.

This paper proposes and presents one approach for people identification based on image and additional physical parameters, height and step length, of a person comparison. People identification is very important in many areas of human life. There are large number of identification methods (biometric methods) that include a different scope of methods, for example fingerprint identification, hand geometry identification, facial recognition, methods based on human eye identification (retina and iris), gait recognition etc. Most of that methods require some kind of interaction with a person, what could be a problem in many practical applications. The method that does not require any interaction with a person is gait recognition. One approach for a people identification based on gait recognition, that uses silhouettes of a person and parameters of person height and step length, is proposed and presented in this paper.

Biometrics has received extensive attention due to its accuracy and convenience. However, commonly used biometric features still have deficiencies. Facial recognition is a potential threat to privacy, and iris recognition or heartbeat recognition requires specific acquisition equipment, resulting in additional costs. To address this issue, we proposed a novel biometric identification method using image photoplethysmographic (IPPG). IPPG signal is easy collection with a consumer camera and the pixel-averaging operation to extract IPPG signal will remove private facial information. As a vital sign, IPPG signal is difficult to fake using abiotic prostheses. We constructed a CNN-based identification with IPPG (ID-IPPG) to verify the performance of IPPG signals in human identification. The proposed model achieves 97.3% accuracy on IPPG signals dataset containing 12 subjects. Moreover, the model can effectively perform in living body detection. The results demonstrate that IPPG signals contain individual physiological information and the ID-IPPG has high accuracy and security for human identification.

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.

Gait recognition stands as a pivotal biometric technology in individual identification, yet its real-world implementation faces challenges stemming from intra-subject disparities. The task of extracting consistent features to distinguish among various subjects becomes onerous due to factors such as image noise and magnitude divergence, significantly impacting recognition accuracy. In addressing this hurdle, we introduce a groundbreaking approach known as the Spline Magnitude Disparity Cross-Correlated Deep Network, designed to optimize gait recognition efficiency. Our method, the Spline Magnitude Disparity Cross-Correlated Deep Network, operates through two key steps: B-Spline magnitude disparity deformation (BS-MDD) registration and cross-correlated long-short gait recognition modeling. The BS-MDD algorithm employs free-form deformation to approximate the magnitude divergence in gait input, enhancing viewpoint optimization and contributing to the development of the cross-correlated model. By focusing on preserving high-output recognition gates while eliminating forget gates, our approach achieves a heightened recognition rate. Evaluation on the widely utilized CASIA B dataset showcases the superiority of our proposed method over state-of-the-art alternatives in terms of the true positive rate, false-positive rate, recognition time, and overall recognition rate. Notably, our approach elevates the true positive rate by 5% and reduces the false-positive rate by 4%. These results underscore the high effectiveness of our method, demonstrating its capacity to substantially improve the accuracy of gait recognition in practical applications.”

Gait recognition is one of the new biometric techniques. However, gait recognition is easily affected by external factors, resulting in a decrease in the accuracy. Therefore, this paper proposes a gait recognition method based on spatial and temporal multi grained information. This method uses hierarchical network to obtain different receptive fields, focusing on both shallow and deep features of gait. In addition, more spatial and temporal information can be saved to improve the accuracy of gait recognition. Finally, the proposed method is experimentally verified on the CASIA-B dataset. the experimental results show that the accuracy of the proposed method is greatly improved under normal walking, carrying bags and wearing coats.

Iris recognition, a relatively new biometric technology, has great advantages such as variability, stability and security, thus it is the most promising for high security environments. Among its applications are border control in airports and harbors, access control in laboratories and factories, identification for Automatic Teller Machines and restricted access to police evidence rooms. There have been several implementations of security systems using biometric, especially for identification and verification cases. The term biometrics is derived from the Greek words bio (life) and metric (to measure). The pattern used in the biometric is the iris pattern in human eye. The iris pattern has been proved unique for each person. A literature review of the most prominent algorithms implemented in each stage is presented. This paper provides a review of major iris I. Introduction Biometric recognition is an emerging technology which employs the physiological and behavioral characteristics to identify an individual. The physiological characteristics include the iris, fingerprint, face and hand geometry. Voice and signature are categorized as the behavioral characteristics. Among these, the human iris is an annular region between the sclera (the white portion of the eye) and the pupil (the darkest portion of the eye). Iris is gaining lots of attention due its unique pattern. The patterns that give uniqueness to the iris are the coronas, furrows, stripes and so on (1). These patterns thus, distinguish the individual as the genuine and the imposter, and make the iris recognition particularly the promising solution. Iris recognition is a method of biometric authentication that uses pattern-recognition techniques based on high resolution images of the irises of an individual's eyes (16). The main stages of an iris recognition system are: image pre-processing which consists of iris localization, iris normalization; iris feature extraction and template matching (13)(15). It is necessary to obtain the iris region to carry out the feature extraction and the matching. Eyelids and the eyelashes that may cover the iris region are detected and removed. The normalization step is the conversion of Cartesian co-ordinates to the polar co-ordinates. Various image enhancement algorithms can also applied in order to compensate the non uniformity and low contrast characters in the iris portion. Feature extraction is the process of extracting texture from the region of interest and then using these features as parameters for comparing two iris templates. The significant features of the iris are obtained for accurate identification purpose. Template matching compares the user template with the templates from the dataset or the trained dataset using a matching metric. The matching metric will give the measure of similarity between two iris templates. This paper discuses the algorithmic implementation in each stage. Iris image capture stage will not be discussed in this paper. The rest of the stages are discussed next sections including image preprocessing, iris feature extraction and template matching.

Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous approaches including face recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the face recognition problem focus mainly on the static analysis to determine the face recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the face recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a face recognition process under the situation of vague facial features using deep reinforcement learning (DRL) approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of face recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of face recognition (100% at gamma correction γlocated in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.

Gait recognition is a biometric recognition technology that supports long-distance, multi-target recognition with resistance to partial occlusions and does not require active user cooperation; thus, it is more suitable than other technologies for individual identification in mass video surveillance systems. Gait recognition based on deep learning has become the mainstream technology in this field because of its strong self-learning and model prediction abilities. However, there is still a lack of research focusing on actual scenes and application requirements for gait recognition, such as multi-target, real-time, and robust recognition. Therefore, this paper analyzes the basic tasks of deep gait recognition methods and encapsulates the application scope of deep gait recognition. Subsequently, this paper investigates the methods of large-space deep gait recognition from three aspects: image preprocessing, gait feature extraction with deep learning, and classifier and evaluation. In particular, the study investigated and analyzed the gait input templates often used in mass surveillance, auto encoder with deep learning, and performance evaluation indexes for the first time. Finally, the unresolved issues in deep gait recognition are summarized, and suggestions and directions for future research are presented.

Face recognition in video has gained wide attention due to its role in designing surveillance systems. One of the main advantages of video over still frames is that evidence accumulation over multiple frames can provide better face recognition performance. However, surveillance videos are generally of low resolution containing faces mostly in non-frontal poses. Consequently, face recognition in video poses serious challenges to state-of-the-art face recognition systems. Use of 3D face models has been suggested as a way to compensate for low resolution, poor contrast and non-frontal pose. We propose to overcome the pose problem by automatically (i) reconstructing a 3D face model from multiple non-frontal frames in a video, (ii) generating a frontal view from the derived 3D model, and (iii) using a commercial 2D face recognition engine to recognize the synthesized frontal view. A factorization-based structure from motion algorithm is used for 3D face reconstruction. The proposed scheme has been tested on CMU's Face In Action (FIA) video database with 221 subjects. Experimental results show a 40% improvement in matching performance as a result of using the 3D models.

Biometric based attributes are the latest additions to the existing mechanisms used for security of information system and for access control. Among a host of others, face recognition is the most effective biometric system for identification and verification of persons. Face recognition from video has gained attention due to its popularity and ease of use with security systems based on vision and surveillance systems. The automated video based face recognition system provides a huge assortment of challenges as it is necessary to perform facial verification under different viewing conditions. Face recognition in video continues to attract lot of attention from researchers world over hence considerable advances are being recorded in this area. The aim of this chapter is to perform a review of the basic methods used for such techniques and finding the emerging trends of the research in this area. The primary focus is to summarize some well-known methods of face recognition in video sequences for application in biometric security and enumerate the emerging trends.

Automatic face recognition has long been established as one of the most active research areas in computer vision. Face recognition in unconstrained environments remains challenging for most practical applications. In contrast to traditional still-image based approaches, recently the research focus has shifted towards videobased approaches. Video data provides rich and redundant information, which can be exploited to resolve the inherent ambiguities of image-based recognition like sensitivity to low resolution, pose variations and occlusion, leading to more accurate and robust recognition. Face recognition has also been considered in the content-based video retrieval setup, for example, character-based video search. In this chapter, we review existing research on face recognition and retrieval in video. The relevant techniques are comprehensively surveyed and discussed.