King Hong Cheung

Gait flow image

In this paper, we propose a novel gait representation-gait flow image (GFI) for use in gait recognition. This representation will further improve recognition rates. The basis of GFI is the binary silhouette sequence. GFI is generated by using an optical flow field without constructing any model. The performance of the proposed representation was evaluated and compared with the other representations, such as gait energy image (GEI), experimentally on the USF data set. The USF data set is a public data set in which the image sequences were captured outdoors. The experimental results show that the proposed representation is efficient for human identification. The average recognition rate of GFI is better than that of the other representations in direct matching and dimensional reduction approaches. In the direct matching approach, GFI achieved an average identification rate 42.83%, which is better than GEI by 3.75%. In the dimensional reduction approach, GFI achieved an average identification rate 43.08%, which is better than GEI by 1.5%. The experimental result showed that GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.

An analysis on accuracy of cancelable biometrics based on biohashing

Cancelable biometrics has been proposed for canceling and re-issuing biometric templates and for protecting privacy in biometrics systems. Recently, new cancelable biometric approaches are proposed based on BioHashing, which are random transformed feature-based cancelable biometrics. In this paper, we consider the accuracy of one of the cancelable biometrics based on BioHashing and face. Through this analysis, as an illustration, we would like to raise an issue to be considered in cancelable biometrics: accuracy may be traded for biometrics being cancelable.

Revealing the secret of facehashing

Biometric authentication has attracted substantial attention over the past few years. It has been reported recently that a new technique called FaceHashing, which is proposed for personal authentication using face images, has achieved perfect accuracy and zero equal error rates (EER). In this paper, we are going to reveal that the secret of FaceHashing in achieving zero EER is based on a false assumption. This is done through simulating the claimants’ experiments. Thus, we would like to alert the use of “safe” token.

Does EigenPalm work? A System and Evaluation Perspective

Recently, there are keen interests in EigenPalm, which, collectively, refers to those methods that extract palmprint features directly from the appearance by means of principal component analysis (PCA) for (dis)similarity matching. Encouraging results have been reported with the use of EigenPalm. However, we find a different story under a system and evaluation perspective. In this paper, we would like to introduce three issues that should be considered: the effects of templates from two different sessions, the effects of identical twins and the effects of unseen subjects. They are missing in the previous studies of EigenPalm

Hierarchical content-based image retrieval by dynamic indexing and guided search

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

A new approach to personal identification in large databases by hierarchical palmprint coding with multi-features

This paper presents a new approach to personal identification using palmprints. To tackle the key issues such as feature extraction, representation, indexing, similarity measurement and fast search for the best match, we propose a hierarchical multi-feature coding scheme to facilitate coarse-to-fine matching for efficient and effective palmprint verification and identification in a large database. In contrast to the existing systems that employ a fixed mechanism for feature extraction and similarity measurement, we extract multiple features and adopt different matching criteria at different levels to achieve high performance by coarse-to-fine guided search. Our experimental results demonstrate the feasibility and effectiveness of the proposed method.

A robust automatic face recognition system for real‐time personal identification

Purpose – Aims to develop an efficient and robust system for real‐time personal identification by automatic face recognition.Design/methodology/approach – A wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme are introduced to improve the computation efficiency in the face detection task. In addition, a Gabor‐based low feature dimensional pattern is proposed to deal with the face recognition problem.Findings – The proposal of a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme is effective to improve the computation efficiency in the face detection task. The introduction of a low feature dimensional pattern is powerful to cope with the transformed appearance‐based face recognition problem. In addition, the use of aggregated Gabor filter responses to represent face images provides a better solution to face feature extraction.Research limitations/implications – Provides guidance in the design of automatic face recognition system for real‐time personal identification...

An integration of biometrics and mobile computing for personal identification

This paper presents a new approach to efficient and effective personal identification for the security of network access by combining techniques in biometrics and mobile computing. To overcome the limitations of the existing password-based authentication services on the Internet, we propose a dynamic feature selection scheme to extract multiple personal features and integrate them in a hierarchical structure for fast and reliable identity authentication. To increase the speed and flexibility of the process, we use mobile agents as a navigational tool for parallel implementation in a distributed environment, which includes hierarchical biometric feature extraction, multiple feature integration, dynamic biometric data indexing and guided search.

A Mobile Augmentative and Alternative Communication (MAAC) Application for Disabilities

Communication board is one of Augmentative and Alternative Communication (AAC) which provides a large amount of pictures classified under various categories. It is a useful instrument for disabled people, who are suffering from speech and language impairment, to facilitate their communication by simply selecting pictures. The traditional communication board would assist the users to communicate with the others. However, there are a number of limitations of the communication board such as large in size and difficult to carry. Because of the advanced technology, it is inexpensive to purchase a mobile device. In this paper, we present our mobile augmentative and alternative communication application, namely MAAC app, for disabled people. The MAAC app runs on the Android-based mobile devices such as smartphones or tablets. This application is suitable for disabled people who are suffered from cerebral palsy, spasm and stroke. We performed user acceptance test (UAT) with domain experts on the application and the overall comment is positive.

Dynamic Feature Selection and Coarse-To-Fine Search for Content-Based Image Retrieval

We present a new approach to content-based image retrieval by addressing three primary issues: image indexing, similarity measure, and search methods. The proposed algorithms include: an image data warehousing structure for dynamic image indexing; a statistically based feature selection procedure to form flexible similarity measures in terms of the dominant image features; and a feature component code to facilitate query processing and guide the search for the best matching. The experimental results demonstrate the feasibility and effectiveness of the proposed method.