Uncontrolled face recognition by individual stable neural network

There usually exist diverse variations in face images taken under uncontrolled conditions. Most previous work on face recognition focuses on particular variations and usually assume the absence of others. Such work is called controlled face recognition. Instead of the ‘divide and conquer’ strategy adopted by controlled face recognition, this paper presents one of the first attempts directly aiming at uncontrolled face recognition. The solution is based on Individual Stable Neural Network (ISNN) proposed in this paper. ISNN can map a face image into the so-called Individual Stable Space (ISS), the feature space that only expresses personal characteristics, which is the only useful information for recognition. There are no restrictions for the face images fed into ISNN. Moreover, unlike many other robust face recognition methods, ISNN does not require any extra information (such as view angle) other than the personal identities during training. These advantages of ISNN make it a very practical approach for uncontrolled face recognition. In the experiments, ISNN is tested on two large face databases with vast variations and achieves the best performance compared with several popular face recognition techniques.

Individual discriminative subspace for face recognition under uncontrolled conditions

Most face recognition (FR) algorithms require the face images to satisfy certain restrictions in various aspects like view angle, illumination, occlusion, etc. But what is needed in general is the techniques that can recognize any face images recognizable by human beings. This paper provides one potential solution to this problem. A method named Individual Discriminative Subspace (IDS) is proposed for robust face recognition under uncontrolled conditions. IDS is the subspace where only the images from one particular person converge around the origin while those from others scatter. Each IDS can be used to distinguish one individual from others. There is no restriction on the face images fed into the algorithm, which makes it practical for real-life applications. In the experiments, IDS is tested on two large face databases with extensive variations and performs significantly better than 12 existing FR techniques.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

Individual stable space : an approach to face recognition under uncontrolled conditions

There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a ldquodivide and conquerrdquo strategy, this paper attempts to directly address face recognition under uncontrolled conditions. The key is the individual stable space (ISS), which only expresses personal characteristics. A neural network named ISNN is proposed to map a raw face image into the ISS. After that, three ISS-based algorithms are designed for FR under uncontrolled conditions. There are no restrictions for the images fed into these algorithms. Moreover, unlike many other FR techniques, they do not require any extra training information, such as the view angle. These advantages make them practical to implement under uncontrolled conditions. The proposed algorithms are tested on three large face databases with vast variations and achieve superior performance compared with other 12 existing FR techniques.

Facial image analysis

This thesis focuses on novel technologies for facial image analysis, which involves three topics: face recognition under uncontrolled conditions, automatic facial age estimation, and context-aware fusion of face and gait. They are either key issues bridging laboratorial research and real applications, or innovative problems that have barely been studied before.

A review of vision-based gait recognition methods for human identification

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.

On the classification of image features

While the primary purpose of edge detection schemes is to be able to produce an edge map of a given image, the ability to distinguish between different feature types is also of importance. In this paper we examine feature classification based on local energy detection and show that local energy measures are intrinsically capable of making this classification because of the use of odd and even filters. The advantage of feature classification is that it allows for the elimination of certain feature types from the edge map, thus simplifying the task of object recognition.

Model-based character recognition in low resolution

We propose a combined character separation and recognition approach for low-resolution images of alphanumeric text. By synthesising the image formation process a set of low-resolution templates is created for each character. Cluster algorithms and normalised cross-correlation are then applied to match these templates and thereby allowing both character separation and recognition to be achieved at the same time. Thus characters are recognised using their low-resolution appearance only without applying image enhancement methods. Experiments showed that this approach is able to recognise low-resolution alphanumeric text of down to 5 pixels in size.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Application of the DCT energy histogram for face recognition

In this paper, we investigate the face recognition problem via energy histogram of the DCT coefficients. Several issues related to the recognition performance are discussed, In particular the issue of histogram bin sizes and feature sets. In addition, we propose a technique for selecting the classification threshold incrementally. Experimentation was conducted on the Yale face database and results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall. Furthermore, it demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm.

Using a circular grid for offline handwritten character recognition

Offline handwritten recognition is more challenging as indicated by the recognition technologies. This study demonstrates significantly higher rates recognition when compared with other comparable studies. In this paper, we present a circular grid zoning method applied on Polar transformation recognition system. It compares the circular grid zoning (CGZ) and standard zoning (SZ) feature extraction method on Polar and Cartesian coordinate system. We report recognition rates of 92.3%, which are considerably higher than previous studies of zoning based Polar transformation system (86.6%) and zoning based Cartesian recognition system (80.6%). Based on the finding, we propose that our circular grid zoning based Polar transformation system may provide improved classification rates for complex offline handwritten recognition.

The effect of teaching conceptual and image metaphors to EFL learners

Foreign language learners encounter difficulties utilizing metaphorical expressions in everyday language, particularly because the use of metaphors in the discourse context is inescapable. In this study, efforts have been made to provide an effective way for the Iranian language learners to acquire and employ conceptual and image metaphors. The instruction took place on 60 junior students studying at the University of Petroleum Engineering at Ahvaz, a southern city in Iran, who were selected on the basis of their high marks on an English proficiency test. Forty metaphors of both types were presented to the participants during 10 sessions of instruction. To collect data on the learners' performance, a 30- item sentence completion test requiring 'word-given' and 'recognition' type responses was prepared and administered. The results of a statistical t-test indicated no significant difference in the rate of acquiring conceptual and image metaphors (P>.05). Therefore, both metaphors can be successfully taught concurrently with little difficulty.

Optimal metric selection for improved multi-pose face recognition with group information

We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is the wrong choice of the ℓ2 norm, which does not match with data statistics as these residual values may be considerably non-Gaussian. We propose an explicit but effective solution using ℓp norm and explain theoretically and numerically why such metric norm would be able to suppress outliers and thus can significantly improve classification performance comparable to the state-of-arts algorithms on some challenging datasets