Entropy-based Iterative Face Classification

Abstract. This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropybased measure, that contain the most useful clusters. The best match to the test face is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem

An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways

Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of fasteners on the corresponding track in high-speed(up to 400 km/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km/h

Image processing techniques for human-centered interaction and for video analysis

The main research focus of this dissertation pertains to developing novel image processing and artificial intelligence techniques in order to perform face recognition, face verification, facial expression classification, and scene change detection in video sequences. Initially, a face recognition method is proposed, which aims to determine the identity of a test face by solving a number of simplified classification problems. This is achieved by applying dynamic training to a multi-step clustering scheme, during which discriminant clusters are created. The dynamic training allows creating a set of test-face-specific subspaces, which improves performance. Next, a face verification method is proposed. It applies a piecewise linear discriminant analysis process on similarity feature vectors and properly weighs the resulting discriminant hyper-planes. This method curbs problems related to insufficient numbers of training data and its expected classification performance is investigated via a series of simulations. Then, proper combinations of the various similarity scores are determined and used to produce a more accurate decision. In addition, a facial expression classification method is proposed. It operates by solving a number of two-class problems, a choice that is analytically justified. Then, it makes use of a class separability measure and applies an iterative process in order to extract high-quality features. Next, a classification scheme is developed where, during each of its steps, the most reliable classifier is identified and used in order to produce an more accurate decision. Lastly, a scene change detection method is proposed. It produces a set of enhanced eigen-audioframes that relate to a subspace where background noise changes are easily detected. Moreover, visual information is employed in order to align the audio scene change indications to neighboring video shot changes. In addition, certain video special-effects that are commonly used during scene changes are detected. By using various acoustic features to verify the scene change indications, the accuracy of this method increases further

METHODS FOR IMPROVING DISCRIMINANT ANALYSIS FOR FACE AUTHENTICATION

A novel algorithm � that can be used to boost the performance of face authentication methods that utilize Fisher’s criterion is presented. The algorithm is applied to matching error data and provides a general solution for overcoming the “small sample size ” (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of locally linear discriminant models is used in order to calculate discriminant weights in a more accurate way than the traditional linear discriminant analysis (LDA) methodology. Additionally, defective discriminant coefficients are identified and reestimated. The second phase defines proper combinations for person-specific matching scores and describes an outlier removal process that enhances the classification ability. Our technique was tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the authentication algorithm’s performance. 1

Wireless Sensors Known Location Unknown Location

link central computer