Catálogo de publicaciones - libros

This paper proposes a novel, simple and efficient method for face segmentation which works by coupling face detection and segmentation in a single framework. We use the OBJCUT [1] formulation that allows for a smooth combination of object detection and Markov Random Field for segmentation, to produce a real-time face segmentation. It should be noted that our algorithm is extremely efficient and runs in real time.

In this paper, a novel representation called the is proposed for face recognition. The subband face is generated from selected subbands obtained using wavelet decomposition of the original face image. It is surmised that certain subbands contain information that is more significant for discriminating faces than other subbands. The problem of subband selection is cast as a combinatorial optimization problem and genetic algorithm (GA) is used to find the optimum subband combination by maximizing Fisher ratio of the training features. The performance of the GA selected subband face is evaluated using three face databases and compared with other wavelet-based representations.

This paper proposes an object recognition system that is invariant to rotation, translation and scale and can be trained under partial supervision. The system is divided into two sections namely, feature extraction and recognition sections. Feature extraction section uses proposed rotation, translation and scale invariant features. Recognition section consists of a novel Reflex Fuzzy Min-Max Neural Network (RFMN) architecture with “Floating Neurons”. RFMN is capable to learn mixture of labeled and unlabeled data which enables training under partial supervision. Learning under partial supervision is of high importance for the practical implementation of pattern recognition systems, as it may not be always feasible to get a fully labeled dataset for training or cost to label all samples is not affordable. The proposed system is tested on shape data-base available online, Marathi and Bengali digits. Results are compared with “General Fuzzy Min-Max Neural Network” proposed by Gabrys and Bargiela.

In this study, we propose three extended fitting methods to the standard ASM(active shape model). Firstly, profiles are extended from 1D to 2D; Secondly, profiles of different landmarks are constructed individually; Thirdly, length of the profilesis determined adaptively with the change of level during searching, and the displacements in the last level are constrained. Each method and the combination of three methods are tested on the SJTU(Shanghai Jiaotong University) face database. In all cases, compared to the standard ASM, each method improves the accuracy or speed in a way, and the combination of three methods improves the accuracy and speed greatly.

This paper addresses the problem of pose invariant Generic Object Recognition by modeling the perceptual capability of human beings. We propose a novel framework using a combination of appearance and shape cues to recognize the object class and viewpoint (axis of rotation) as well as determine its pose (angle of view). The appearance model of the object from multiple viewpoints is captured using Linear Subspace Analysis techniques and is used to reduce the search space to a few rank-ordered candidates. We have used a decision-fusion based combination of 2D PCA and ICA to integrate the complementary information of classifiers and improve recognition accuracy. For matching based on shape features, we propose the use of distance transform based correlation. A decision fusion using ‘Sum Rule’ of 2D PCA and ICA subspace classifiers, and distance transform based correlation is then used to verify the correct object class and determine its viewpoint and pose. Experiments were conducted on COIL-100 and IGOIL (IITM Generic Object Image Library) databases which contain objects with complex appearance and shape characteristics. IGOIL database was captured to analyze the appearance manifolds along two orthogonal axes of rotation.

3D Face Recognition is an active area of research for past several years. For a 3D face recognition system one would like to have an accurate as well as low cost setup for constructing 3D face model. In this paper, we use Profilometry approach to obtain a 3D face model. This method gives a low cost solution to the problem of acquiring 3D data and the 3D face models generated by this method are sufficiently accurate. We also develop an algorithm that can use the 3D face model generated by the above method for the recognition purpose.

Techniques that can introduce low dimensional feature representation with enhanced discriminatory power are important in face recognition systems. This paper presents one of the symbolic factor analysis method i.e., symbolic Linear Discriminant Analysis (symbolic LDA) method for face representation and recognition. Classical factor analysis methods extract features, which are single valued in nature to represent face images. These single valued variables may not be able to capture variation of each feature in all the images of same subject; this leads to loss of information. The symbolic Linear Discriminant Analysis Algorithm extracts most discriminating interval type features; they optimally discriminate among the classes represented in the training set. The proposed method has been successfully tested for face recognition using two databases, ORL and Yale Face database. The effectiveness of the proposed method is shown in terms of comparative performance against popular classical factor analysis methods such as eigenface method and Linear Discriminant Analysis method. Experimental results show that symbolic LDA outperforms the classical factor analysis methods.

This paper proposes a new method of feature extraction called two-dimensional optimal transform (2D-OPT) useful for appearance based object recognition. The 2D-OPT method provides a better discrimination power between classes by maximizing the distance between class centers. We first argue that the proposed 2D-OPT method works in the row direction of images and subsequently we propose an alternate 2D-OPT which works in the column direction of images. To straighten out the problem of massive memory requirements of the 2D-OPT method and as well the alternate 2D-OPT method, we introduce bi-projection 2D-OPT. The introduced bi-projection 2D-OPT method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the standard PCA/2D-PCA/Generalized 2D-PCA method, and the same has been revealed through extensive experimentations conducted on COIL-20 dataset and AT&T face dataset.

This paper presents a novel approach to construct an eigen space representation from limited number of views, which is equivalent to the one obtained from large number of images captured from multiple view points. This procedure implicitly incorporates a novel view synthesis algorithm in the eigen space construction process. Inherent information in an appearance representation is enhanced using geometric computations. We experimentally verify the performance for orthographic, affine and projective camera models. Recognition results on the COIL and SOIL image database are promising.

Pose and illumination variations are the most dominating and persistent challenges haunting face recognition, leading to various highly-complex 2D and 3D model based solutions. We present a novel transform vector quantization (TVQ) method which is fast and accurate and yet significantly less complex than conventional methods. TVQ offers a flexible and customizable way to capture the pose variations. Use of transform such as DCT helps compressing the image data to a small feature vector and judicious use of vector quantization helps to capture the various poses into compact codebooks. A confidence measure based sequence analysis allows the proposed TVQ method to accurately recognize a person in only 3-9 frames (less than 1/2 a second) from a video sequence of images with wide pose variations.