Catálogo de publicaciones - libros

This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of fronto-parallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios.

We present the first framework for detecting, localizing and classifying visual traits of object classes, e.g. gender or age of human faces, from arbitrary viewpoints. We embed all three tasks in a viewpoint-invariant model derived from local scale-invariant features (e.g. SIFT), where features are probabilistically quantified in terms of their occurrence, appearance, geometry and relationship to visual traits of interest. An appearance model is first learned for the object class, after which a Bayesian classifier is trained to identify the model features indicative of visual traits. The advantage of our framework is that it can be applied and evaluated in realistic scenarios, unlike other trait classification techniques that assume data that is single-viewpoint, pre-aligned and cropped from background distraction. Experimentation on the standard color FERET database shows our approach can automatically identify the visual cues in face images linked to the trait of gender. Combined detection, localization and gender classification error rates are a) 15% over a 180-degree range of face viewpoint and b) 13% in frontal faces, lower than other reported results.

Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: () we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; () we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and () we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult illumination conditions: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.

In this paper, we propose a novel appearance-based representation, called Structured Ordinal Feature (SOF). SOF is a binary string encoded by combining eight ordinal blocks in a circle symmetrically. SOF is invariant to linear transformations on images and is flexible enough to represent different local structures of different complexity. We further extend SOF to Multi-scale Structured Ordinal Feature (MSOF) by concatenating binary strings of multi-scale SOFs at a fix position. In this way, MSOF encodes not only microstructure but also macrostructure of image patterns, thus provides a more powerful image representation. We also present an efficient algorithm for computing MSOF using integral images. Based on MSOF, statistical analysis and learning are performed to select most effective features and construct classifiers. The proposed method is evaluated with face recognition experiments, in which we achieve a high rank-1 recognition rate of 98.24% on FERET database.

In this paper we propose a novel boosting based classification algorithm, SODA-Boosting (where SODA stands for Second Order Discriminant Analysis). Unlike the conventional AdaBoost based algorithms widely applied in computer vision, SODA-Boosting does not involve time consuming procedures to search a huge feature pool in every iteration during the training stage. Instead, in each iteration SODA-Boosting efficiently computes discriminative weak classifiers in closed-form, based on reasonable hypotheses on the distribution of the weighted training samples. As an application, SODA-Boosting is employed for image based gender recognition. Experimental results on publicly available FERET database are reported. The proposed algorithm achieved accuracy comparable to state-of-the-art approaches, and demonstrated superior performance to relevant boosting based algorithms.

Small sample size and severe facial variation are two challenging problems for face recognition. In this paper, we propose the SIS (Single Image Subspace) approach to address these two problems. To deal with the former one, we represent each single image as a subspace spanned by its synthesized (shifted) samples, and employ a newly designed subspace distance metric to measure the distance of subspaces. To deal with the latter one, we divide a face image into several regions, compute the contribution scores of the training samples based on the extracted subspaces in each region, and aggregate the scores of all the regions to yield the ultimate recognition result. Experiments on well-known face databases such as AR, Extended YALE and FERET show that the proposed approach outperforms some renowned methods not only in the scenario of , but also in the scenario of with significant facial variations.

Integral projections reduce the size of input data by transforming 2D images into significantly simpler 1D signals, while retaining useful information to solve important computer vision problems like object detection, location, and tracking. However, previous attempts typically rely on simple heuristic analysis such as searching for minima or maxima in the resulting projections. We introduce a more rigorous and formal modeling framework based on a small set of integral projections –thus, we will call them – and show that this model-based analysis overcomes many of the difficulties and limitations of alternative projection methods. The proposed approach proves to be particularly adequate for the specific domain of human face processing. The problems of face detection, facial feature location, and tracking in video sequences are studied under the unifying proposed framework.

Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results.

In this paper, we propose a unified scheme of subspace and distance metric learning under the Bayesian framework for face recognition. According to the local distribution of data, we divide the k-nearest neighbors of each sample into the intra-person set and the inter-person set, and we aim to learn a distance metric in the embedding subspace, which can make the distances between the sample and its intra-person set smaller than the distances between it and its inter-person set. To reach this goal, we define two variables, that is, the intra-person distance and the inter-person distance, which are from two different probabilistic distributions, and we model the goal with minimizing the overlap between two distributions. Inspired by the Bayesian classification error estimation, we formulate it by minimizing the Bhattachyrra coefficient between two distributions. The power of the proposed approach are demonstrated by a series of experiments on the CMU-PIE face database and the extended YALE face database.

Pose variance remains a challenging problem for face recognition. In this paper, a scheme including image synthesis and recognition is proposed to improve the performance of automatic face recognition system. In the image synthesis part, a series of pose-variant images are produced based on three images respectively with front, left-profile, right-profile poses, and are added into the gallery in order to overcome the pose inconsistence between probes and images in the database. In the recognition part, a multi-level fusion method based on Gabor-combined features and gray-intensity features (GCGIF) is presented. Both amplitude features and phase features extracted through Gabor filters are utilized. Fusion is introduced in both the face representation level and the confidence level. Experiment results show that the integrated scheme achieve superior recognition performance.