Appearance-Based Tracking and Face Identification in Video Sequences.

We present a technique for face recognition in videos. We are able to recognise a face in a video sequence, given a single gallery image. By assuming that the face is in an approximately frontal position, we jointly model changes in facial appearance caused by identity and illumination. The identity of a face is described by a vector of appearance parameters. We use an angular distance to measure the similarity of faces and a probabilistic procedure to accumulate evidence for recognition along the sequence. We achieve 93.8% recognition success in a set of 65 sequences of 6 subjects from the La Cascia and Sclaroff database

Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition

Este trabajo presenta una solución al problema del reconocimiento del género de un rostro humano a partir de una imagen. Adoptamos una aproximación que utiliza la cara completa a través de la textura de la cara normalizada y redimensionada como entrada a un clasificador Näive Bayes. Presentamos la técnica de Análisis de Componentes Principales Probabilístico Condicionado-a-la-Clase (CC-PPCA) para reducir la dimensionalidad de los vectores de características para la clasificación y asegurar la asunción de independencia para el clasificador. Esta nueva aproximación tiene la deseable propiedad de presentar un modelo paramétrico sencillo para las marginales. Además, este modelo puede estimarse con muy pocos datos. En los experimentos que hemos desarrollados mostramos que CC-PPCA obtiene un 90% de acierto en la clasificación, resultado muy similar al mejor presentado en la literatura---ABSTRACT---This paper presents a solution to the problem of recognizing the gender of a human face from an image. We adopt a holistic approach by using the cropped and normalized texture of the face as input to a Naïve Bayes classifier. First it is introduced the Class-Conditional Probabilistic Principal Component Analysis (CC-PPCA) technique to reduce the dimensionality of the classification attribute vector and enforce the independence assumption of the classifier. This new approach has the desirable property of a simple parametric model for the marginals. Moreover this model can be estimated with very few data. In the experiments conducted we show that using CCPPCA we get 90% classification accuracy, which is similar result to the best in the literature. The proposed method is very simple to train and implement

Efficient Model-Based 3D Tracking of Deformable Objects

Efficient incremental image alignment is a topic of renewed interest in the computer vision community because of its applications in model fitting and model-based object tracking. Successful compositional procedures for aligning 2D and 3D models under weak-perspective imaging conditions have already been proposed. Here we present a mixed compositional and additive algorithm which is applicable to the full projective camera case

A model of brightness variations due to illumination changes and non-rigid motion using spherical harmonics

Pixel brightness variations in an image sequence depend both on the objects ‘surface reflectance and on the motion of the camera and object. In the case of rigid shapes some proposed models have been very successful explaining the relation among these strongly coupled components. On the other hand, shapes which deform pose new challenges since the relation between pixel brightness variation with non-rigid motion is not yet clear. In this paper, we introduce a new model which describes brightness variations with two independent components represented as linear basis shapes. Lighting influence is represented in terms of Spherical Harmonics and non-rigid motion as a linear model which represents image coordinates displacement. We then propose an efficient procedure for the estimation of this image model in two distinct steps. First, shape normal’s and albedo are estimated using standard photometric stereo on a sequence with varying lighting and no deformable motion. Then, given the knowledge of the object’s shape normal’s and albedo, we efficiently compute the 2D coordinates bases by minimizing image pixel residuals over an image sequence with constant lighting and only non-rigid motion. Experiments on real tests show the effectiveness of our approach in a face modelling context

Real-time facial expression recognition with illumination-corrected image sequences

We present a real-time user-independent computer vision system that processes a sequence of images of a front-facing human face and recognizes a set of facial expressions at 30fps. We track the face using an efficient appearance-based face tracker. We model changes in illumination with a user independent appearance-based model. In our approach to facial expression classification, the image of a face is represented by a low dimensional vector that results from projecting the illumination corrected image onto a low dimensional expression manifold. In the experiments conducted we show that the system is able to recognize facial expressions in image sequences with large facial motion and illumination changes

An Empirical Comparison of Graph-based Dimensionality Reduction Algorithms on Facial Expression Recognition Tasks

Facial expression recognition is a topic of interest both in industry and academia. Recent approaches to facial expression recognition are based on mapping expressions to low dimensional manifolds. In this paper we revisit various dimensionality reduction algorithms using a graph-based paradigm. We compare eight dimensionality reduction algorithms on a facial expression recognition task. For this task, experimental results show that although Linear Discriminant Analysis (LDA)is the simplest and oldest supervised approach, its results are comparable to more flexible recent algorithms.LDA, on the other hand, is much simpler to tune, since it only depends on one parameter

Multi-class Boosting for imbalanced data.

We consider the problem of multi-class classification with imbalanced data-sets. To this end, we introduce a cost-sensitive multi-class Boosting algorithm (BAdaCost) based on a generalization of the Boosting margin, termed multi-class cost-sensitive margin. To address the class imbalance we introduce a cost matrix that weighs more hevily the costs of confused classes and a procedure to estimate these costs from the confusion matrix of a standard 0|1-loss classifier. Finally, we evaluate the performance of the approach with synthetic and real data-sets and compare our results with the AdaC2.M1 algorithm

Efficient illumination independent appearance-based face tracking

One of the major challenges that visual tracking algorithms face nowadays is being able to cope with changes in the appearance of the target during tracking. Linear subspace models have been extensively studied and are possibly the most popular way of modelling target appearance. We introduce a linear subspace representation in which the appearance of a face is represented by the addition of two approxi- mately independent linear subspaces modelling facial expressions and illumination respectively. This model is more compact than previous bilinear or multilinear ap- proaches. The independence assumption notably simplifies system training. We only require two image sequences. One facial expression is subject to all possible illumina- tions in one sequence and the face adopts all facial expressions under one particular illumination in the other. This simple model enables us to train the system with no manual intervention. We also revisit the problem of efficiently fitting a linear subspace-based model to a target image and introduce an additive procedure for solving this problem. We prove that Matthews and Baker’s Inverse Compositional Approach makes a smoothness assumption on the subspace basis that is equiva- lent to Hager and Belhumeur’s, which worsens convergence. Our approach differs from Hager and Belhumeur’s additive and Matthews and Baker’s compositional ap- proaches in that we make no smoothness assumptions on the subspace basis. In the experiments conducted we show that the model introduced accurately represents the appearance variations caused by illumination changes and facial expressions. We also verify experimentally that our fitting procedure is more accurate and has better convergence rate than the other related approaches, albeit at the expense of a slight increase in computational cost. Our approach can be used for tracking a human face at standard video frame rates on an average personal computer

Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base