Jamal Hussain Shah

A NOVEL BIOMECHANICS-BASED APPROACH FOR PERSON RE-IDENTIFICATION BY GENERATING DENSE COLOR SIFT SALIENCE FEATURES

Currently, identifying humans using biomechanics-based approaches has gained a lot of significance for person re-identification. Biomechanics-based approaches use knee-hip angle–angle relationships and body movements for person re-identification. Generally, biomechanics of human walking and running is used for person re-identification. In fact, person re-identification is a complex and important task in academia as well as industry and remains an unsolved issue in the computer vision field. The subjects most commonly addressed regarding person re-identification include significant feature extraction that can function accurately with invariant appearance and robust classification. In this study, a significant color feature descriptor is proposed by combining dense color-SIFT and global convex hull salience region features. First convex hull boundary points are detected using the SIFT technique. Furthermore, it is extended with Grubb’s outlier test to eliminate the outlier points detected by SIFT and mark t...

Robust Face Recognition Technique under Varying Illumination

Face recognition is one of a complex biometrics in the field of pattern recognition due to the constraints imposed by variation in the appearance of facial images. These changes in appearance are affected by variation in illumination, expression or occlusions etc. Illumination can be considered a complex problem in both indoor and outdoor pattern matching. Literature studies have revealed that two problems of textural based illumination handling in face recognition seem to be very common. Firstly, textural values are changed during illumination normalization due to increase in the contrast that changes the original pixels of face. Secondly, it minimizes the distance between inter- classes which increases the false acceptance rates. This paper addresses these issues and proposes a robust algorithm that overcomes these limitations. The limitations are resolved through transforming pixels from non- illumination side to illuminated side. It has been revealed that proposed algorithm produced better results as compared to existing related algorithms.

Multi-camera handoff for person re-identification

Abstract During recent years, the demand for automated video surveillance has become the subject of much attention. Currently, video surveillance is an essential part of security and monitoring in banks, streets, buildings, department stores, stadiums, highways, railway stations, and crowded gatherings. One important reason for using video surveillance is to counter terrorism. In this context, a multi-camera handoff system for person re-identification using a color-based global appearance model is presented. We address two main factors that affect re-identification performance in this paper: (1) robust features (viewpoint orientation and indoor/outdoor cinematography) and (2) re-identification or classification. A hexagonal-superpixel method is introduced to minimize the false recognition rate due to the above-mentioned challenges. First we detect the human or object in a video and use a mixture of Gaussian models extended with a trajectory-learning algorithm to solve the trajectory dilemma. Second, a novel feature descriptor is proposed in which sensitive features are extracted using the proposed Color Hexagonal-SIFT and Color Histogram Features (CHF) methods. Third, we generate a combined appearance feature descriptor for re-identifying the human in different scenes. Finally, we introduce a Time-Tree-based learning method to minimize the gallery set׳s overhead and increase the accuracy of the re-identification results. The proposed framework is tested on videos taken using three cameras placed in three different locations. We also tested this framework on publically available data from the i-LIDS and VIPeR datasets. The results show outstanding speed and re-identification accuracy in dealing with the above-mentioned issues.

Multi-feature fusion based re-ranking for person re-identification

In computer vision applications such as person re-identification the optimization of rank list is an important issue. In order to address this issue, a multi-feature fusion based re-ranking technique is proposed. In most of the conventional methods, a long feature vector is formulated from a single modality. Whereas, in the proposed approach, multiple features from the image are extracted and combined into a unified/hybrid vector. Later a joint feature vector is presented after fusion. The Mahalanobis distance is calculated for checking the similarity between the image pairs. A tree based re-ranking algorithm is also proposed that used this combined feature vector and the distance metric. Therefore, by effective use of each feature type, better re-rank can be achieved. We assessed the proposed method on publically available datasets VIPeR and ETHZ. Experimental results demonstrate that the presented approach performs well than exploiting each individual feature.