Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries.

Emotion recognition: The role of featural and configural face information

Several studies investigated the role of featural and configural information when processing facial identity. A lot less is known about their contribution to emotion recognition. In this study, we addressed this issue by inducing either a featural or a configural processing strategy (Experiment 1) and by investigating the attentional strategies in response to emotional expressions (Experiment 2). In Experiment 1, participants identified emotional expressions in faces that were presented in three different versions (intact, blurred, and scrambled) and in two orientations (upright and inverted). Blurred faces contain mainly configural information, and scrambled faces contain mainly featural information. Inversion is known to selectively hinder configural processing. Analyses of the discriminability measure (A′) and response times (RTs) revealed that configural processing plays a more prominent role in expression recognition than featural processing, but their relative contribution varies depending on the emotion. In Experiment 2, we qualified these differences between emotions by investigating the relative importance of specific features by means of eye movements. Participants had to match intact expressions with the emotional cues that preceded the stimulus. The analysis of eye movements confirmed that the recognition of different emotions rely on different types of information. While the mouth is important for the detection of happiness and fear, the eyes are more relevant for anger, fear, and sadness.

Face detection in C#

Actualmente la detección del rostro humano es un tema difícil debido a varios parámetros implicados. Llega a ser de interés cada vez mayor en diversos campos de aplicaciones como en la identificación personal, la interface hombre-máquina, etc. La mayoría de las imágenes del rostro contienen un fondo que se debe eliminar/discriminar para poder así detectar el rostro humano. Así, este proyecto trata el diseño y la implementación de un sistema de detección facial humana, como el primer paso en el proceso, dejando abierto el camino, para en un posible futuro, ampliar este proyecto al siguiente paso, que sería, el Reconocimiento Facial, tema que no trataremos aquí. En la literatura científica, uno de los trabajos más importantes de detección de rostros en tiempo real es el algoritmo de Viola and Jones, que ha sido tras su uso y con las librerías de Open CV, el algoritmo elegido para el desarrollo de este proyecto. A continuación explicaré un breve resumen sobre el funcionamiento de mi aplicación. Mi aplicación puede capturar video en tiempo real y reconocer el rostro que la Webcam captura frente al resto de objetos que se pueden visualizar a través de ella. Para saber que el rostro es detectado, éste es recuadrado en su totalidad y seguido si este mueve. A su vez, si el usuario lo desea, puede guardar la imagen que la cámara esté mostrando, pudiéndola almacenar en cualquier directorio del PC. Además, incluí la opción de poder detectar el rostro humano sobre una imagen fija, cualquiera que tengamos guardada en nuestro PC, siendo mostradas el número de caras detectadas y pudiendo visualizarlas sucesivamente cuantas veces queramos. Para todo ello como bien he mencionado antes, el algoritmo usado para la detección facial es el de Viola and Jones. Este algoritmo se basa en el escaneo de toda la superficie de la imagen en busca del rostro humano, para ello, primero la imagen se transforma a escala de grises y luego se analiza dicha imagen, mostrando como resultado el rostro encuadrado. ABSTRACT Currently the detection of human face is a difficult issue due to various parameters involved. Becomes of increasing interest in various fields of applications such as personal identification, the man-machine interface, etc. Most of the face images contain a fund to be removed / discriminate in order to detect the human face. Thus, this project is the design and implementation of a human face detection system, as the first step in the process, leaving the way open for a possible future, extend this project to the next step would be, Facial Recognition , a topic not covered here. In the literature, one of the most important face detection in real time is the algorithm of Viola and Jones, who has been after use with Open CV libraries, the algorithm chosen for the development of this project. I will explain a brief summary of the performance of my application. My application can capture video in real time and recognize the face that the Webcam Capture compared to other objects that can be viewed through it. To know that the face is detected, it is fully boxed and followed if this move. In turn, if the user may want to save the image that the camera is showing, could store in any directory on your PC. I also included the option to detect the human face on a still image, whatever we have stored in your PC, being shown the number of faces detected and can view them on more times. For all as well I mentioned before, the algorithm used for face detection is that of Viola and Jones. This algorithm is based on scanning the entire surface of the image for the human face, for this, first the image is converted to gray-scale and then analyzed the image, showing results in the face framed.

Tag detection for preventing unauthorized face image processing

A new technology is being proposed as a solution to the problem of unintentional facial detection and recognition in pictures in which the individuals appearing want to express their privacy preferences, through the use of different tags. The existing methods for face de-identification were mostly ad hoc solutions that only provided an absolute binary solution in a privacy context such as pixelation, or a bar mask. As the number and users of social networks are increasing, our preferences regarding our privacy may become more complex, leaving these absolute binary solutions as something obsolete. The proposed technology overcomes this problem by embedding information in a tag which will be placed close to the face without being disruptive. Through a decoding method the tag will provide the preferences that will be applied to the images in further stages.

The Changing Face of the Epidemiology of Tuberculosis due to Molecular Strain Typing: A Review

About one third of the world population is infected with tubercle bacilli, causing eight million new cases of tuberculosis (TB) and three million deaths each year. After years of lack of interest in the disease, World Health Organization recently declared TB a global emergency and it is clear that there is need for more efficient national TB programs and newly defined research priorities. A more complete epidemiology of tuberculosis will lead to a better identification of index cases and to a more efficient treatment of the disease. Recently, new molecular tools became available for the identification of strains of Mycobacterium tuberculosis (M. tuberculosis), allowing a better recognition of transmission routes of defined strains. Both a standardized restriction-fragment-length-polymorphism-based methodology for epidemiological studies on a large scale and deoxyribonucleic acids (DNA) amplification-based methods that allow rapid detection of outbreaks with multidrug-resistant (MDR) strains, often characterized by high mortality rates, have been developed. This review comments on the existing methods of DNA-based recognition of M. tuberculosis strains and their peculiarities. It also summarizes literature data on the application of molecular fingerprinting for detection of outbreaks of M. tuberculosis, for identification of index cases, for study of interaction between TB and infection with the human immunodeficiency virus, for analysis of the behavior of MDR strains, for a better understanding of risk factors for transmission of TB within communities and for population-based studies of TB transmission within and between countries

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

Conditions for Viewpoint Dependent Face Recognition

Poggio and Vetter (1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly bilaterally symmetric objects. Learning a side-view--which always has a symmetric view--should allow for better generalization performances than learning the frontal view. Two psychophysical experiments tested these predictions. Stimuli were views of shaded 3D models of laser-scanned faces. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the symmetry hypothesis: Learning a side view allowed better generalization performances than learning the frontal view.

An Empirical Comparison of SNoW and SVMs for Face Detection

Impressive claims have been made for the performance of the SNoW algorithm on face detection tasks by Yang et. al. [7]. In particular, by looking at both their results and those of Heisele et. al. [3], one could infer that the SNoW system performed substantially better than an SVM-based system, even when the SVM used a polynomial kernel and the SNoW system used a particularly simplistic 'primitive' linear representation. We evaluated the two approaches in a controlled experiment, looking directly at performance on a simple, fixed-sized test set, isolating out 'infrastructure' issues related to detecting faces at various scales in large images. We found that SNoW performed about as well as linear SVMs, and substantially worse than polynomial SVMs.

Face Detection in Still Gray Images

We present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We first consider the problem of detecting the whole face pattern by a single SVM classifer. In this context we compare different types of image features, present and evaluate a new method for reducing the number of features and discuss practical issues concerning the parameterization of SVMs and the selection of training data. The second part of the paper describes a component-based method for face detection consisting of a two-level hierarchy of SVM classifers. On the first level, component classifers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifer checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face.

Feature Selection for Face Detection

We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM.

Role of color in face recognition

One of the key challenges in face perception lies in determining the contribution of different cues to face identification. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with grayscale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

Relative Contributions of Internal and External Features to Face Recognition

The central challenge in face recognition lies in understanding the role different facial features play in our judgments of identity. Notable in this regard are the relative contributions of the internal (eyes, nose and mouth) and external (hair and jaw-line) features. Past studies that have investigated this issue have typically used high-resolution images or good-quality line drawings as facial stimuli. The results obtained are therefore most relevant for understanding the identification of faces at close range. However, given that real-world viewing conditions are rarely optimal, it is also important to know how image degradations, such as loss of resolution caused by large viewing distances, influence our ability to use internal and external features. Here, we report experiments designed to address this issue. Our data characterize how the relative contributions of internal and external features change as a function of image resolution. While we replicated results of previous studies that have shown internal features of familiar faces to be more useful for recognition than external features at high resolution, we found that the two feature sets reverse in importance as resolution decreases. These results suggest that the visual system uses a highly non-linear cue-fusion strategy in combining internal and external features along the dimension of image resolution and that the configural cues that relate the two feature sets play an important role in judgments of facial identity.

Weightless neural nets for face recognition: a comparison

This paper considers the application of weightless neural networks (WNNs) to the problem of face recognition and compares the results with those provided using a more complicated multiple neural network approach. WNNs have significant advantages over the more common forms of neural networks, in particular in term of speed of operation and learning. A major difficulty when applying neural networks to face recognition problems is the high degree of variability in expression, pose and facial details: the generalisation properties of a WNN can be crucial. In the light of this problem a software simulator of a WNN has been built and the results of some initial tests are presented and compared with other techniques

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

Combining face and facial feature detectors for face detection performance improvement

[EN]In this paper, we experimentally study the combination of face and facial feature detectors to improve face detection performance. The face detection problem, as suggeted by recent face detection challenges, is still not solved. Face detectors traditionally fail in large-scale problems and/or when the face is occluded or di erent head rotations are present. The combination of face and facial feature detectors is evaluated with a public database. The obtained results evidence an improvement in the positive detection rate while reducing the false detection rate. Additionally, we prove that the integration of facial feature detectors provides useful information for pose estimation and face alignment.