Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Personal recognition using hand shape and texture

Author name used in this publication: Ajay Kumar2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI

A comparative analysis of binary patterns with discrete cosine transform for gender classification

This paper presents a comparative analysis of binary patters for gender classification with a novel method of feature transformation for improved accuracy rates. The main requirements of our application are speed and accuracy. We investigate a combination of local binary patterns (LBP), Census Transform (CT) and Modified Census Transform (MCT) applied over the full, top and bottom halves of the face. Gender classification is performed using support vector machines (SVM). A main focus of the investigation is to determine whether or not a 1D discrete cosine transform (DCT) applied directly to the grey level histograms would improve accuracy. We used a public database of faces and run face and eye detection algorithms allowing automatic cropping and normalisation of the images. A set of 120 tests over the entire database demonstrate that the proposed 1D discrete cosine transform improves accuracy in all test cases with small standard deviations. It is shown that using basic versions of the algorithms, LBP is marginally superior to both CT and MCT and agrees with results in the literature for higher accuracy on male subjects. However, a significant result of our investigation is that, by applying a 1D-DCT this bias is removed and an equivalent error rate is achieved for both genders. Furthermore, it is demonstrated that DCT improves overall accuracy and renders CT a superior performance compared to LBP in all cases considered