Finding Faces in Cluttered Scenes using Random Labeled Graph Matching

An algorithm for locating quasi-frontal views of human faces in cluttered scenes is presented. The algorithm works by coupling a set of local feature detectors with a statistical model of the mutual distances between facial features it is invariant with respect to translation, rotation (in the plane), and scale and can handle partial occlusions of the face. On a challenging database with complicated and varied backgrounds, the algorithm achieved a correct localization rate of 95% in images where the face appeared quasi-frontally

Learning non-maximum suppression

Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, features, and the classifier becoming one neural network improved results two-fold on general object detection. One indispensable component is non-maximum suppression (NMS), a post-processing algorithm responsible for merging all detections that belong to the same object. The de facto standard NMS algorithm is still fully hand-crafted, suspiciously simple, and -- being based on greedy clustering with a fixed distance threshold -- forces a trade-off between recall and precision. We propose a new network architecture designed to perform NMS, using only boxes and their score. We report experiments for person detection on PETS and for general object categories on the COCO dataset. Our approach shows promise providing improved localization and occlusion handling.Comment: Added "Supplementary material" titl

A fast and accurate algorithm for facial feature segmentation

Abstrac

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images

Improved facial feature fitting for model based coding and animation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

ANÁLISIS COMPARATIVO DE TÉCNICAS DE RECONOCIMIENTO FACIAL

Los métodos de reconocimiento facial han sido estudiados en años recientes por muchos investigadores en diferentes áreas. Las técnicas más importantes en reconocimiento facial pueden utilizar: Análisis de Componentes Principales (PCA), Patrones Binarios Locales (LBP) y Análisis Discriminante Lineal (LDA). Estas son las técnicas con mejores desempeños en la literatura. Sin embargo, en la literatura actual no existe un estudio comparativo de estas técnicas. En esta tesis son comparadas estas técnicas bajo condiciones específicas en las imágenes de entrada

Face Pose Estimation using a Tree of Boosted Classifiers

Face detection in images or video sequences is a very challenging problem. It has a wide range of applications but at the same time it presents a great number of difficulties, since faces are non-rigid and very changeable objects that can adopt a lot of different poses and with a high inter and intra-person variation and a high sensitivity to lighting conditions. Along this document, a new approach to the face detection and pose estimation problem is given. This approach is based on the method proposed by Viola and Jones in [1] but considering a wide range of face poses, varying the elevation and the out-of-plane rotation, and building specific classifiers for each one. The proposed method can be easily adapted to consider other poses or to detect other objects. Especially, this approach is interesting when an object that can adopt several positions want to be detected, since the partition of the pose space allows to build classifiers specialised in only one or a few poses, which limits the large variance of the global class, the class containing all the poses. In order to facilitate the reproduction of all the processes done in this document, we have used standard face datasets to train and test the system

Iskanje obrazov na osnovi barv s pomočjo statističnih metod razpoznavanja vzorcev

V zadnjem času postaja video nadzor vse pomembnejši in s tem tudi sistemi za iskanje in prepoznavo človeških obrazov na slikah. Zato se v magistrskem delu ukvarjam s problemom iskanja obrazov na slikah. Pri metodah za iskanje obrazov na podlagi barve smo velikokrat omejeni na človeške obraze samo določene polti, same metode pa so tudi zelo odvisne od osvetlitve. V magistrskem delu zato poskušam s pomočjo kromatičnega barvnega prostora odvisnost od osvetlitve zmanjšati. Preizkusil bom različne metode za barvno segmentacijo na osnovi parametričnega in neparametričnega modela. S pomočjo teh modelov bom poskušal modelirati kožno barvo pri različnih osvetlitvah in različnih kožnih polteh. Uspešnost metod bom primerjal z metodo, ki deluje v barvnem prostoru RGB na osnovi eksplicitno določenih mej. Za potrjevanje označenih kožnih regij bom uporabil metodo na osnovi videza, ki nam med vsemi metodami obljublja najboljše rezultate. Izdelal in preizkusil bom metodo BDF, ki na osnovi naučenega vzorca obraza in neobraza s pomočjo Bayesovega klasifikatorja najde frontalne obraze na sivinskih slikah. Glavna slabost metod na osnovi videza je njihova časovna zahtevnost, zato bom poskušal izdelati metodo, ki bo kombinirala pristop na osnovi barv in pristop na osnovi videza. S pomočjo tako izdelane metode bom poskušal doseči hitro in učinkovito iskanje frontalnih obrazov na barvnih slikah

Face tracking with active models for a driver monitoring application

La falta de atención durante la conducción es una de las principales causas de accidentes de tráfico. La \ud \ud monitorización del conductor para detectar inatención es un problema complejo, que incluye elementos fisiológicos y de \ud \ud comportamiento. Un sistema de Visión Computacional para detección de inatención se compone de varios etapas de procesado, y \ud \ud esta tesis se centra en el seguimiento de la cara del conductor. La tesis doctoral propone un nuevo conjunto de vídeos de \ud \ud conductores, grabados en un vehículo real y en dos simuladores realistas, que contienen la mayoría de los comportamientos \ud \ud presentes en la conducción, incluyendo gestos, giros de cabeza, interacción con el sistema de sonido y otras distracciones, \ud \ud y somnolencia. Esta base de datos, RS-DMV, se emplea para evaluar el rendimiento de los métodos que propone la tesis y \ud \ud otros del estado del arte. La tesis analiza el rendimiento de los Modelos Activos de Forma (ASM), y de los Modelos Locales \ud \ud Restringidos (CLM), por considerarlos a priori de interés. En concreto, se ha evaluado el método Stacked Trimmed ASM \ud \ud (STASM), que integra una serie de mejoras sobre el ASM original, mostrando una alta precisión en todas las pruebas cuando \ud \ud la cara es frontal a la cámara, si bien no funciona con la cara girada y su velocidad de ejecución es muy baja. CLM es \ud \ud capaz de ejecutarse con mayor rapidez, pero tiene una precisión mucho menor en todos los casos. El tercer método a evaluar \ud \ud es el Modelado y Seguimiento Simultáneo (SMAT), que caracteriza la forma y la textura de manera incremental, a partir de \ud \ud muestras encontradas previamente. La textura alrededor de cada punto de la forma que define la cara se modela mediante un \ud \ud conjunto de grupos (clusters) de muestras pasadas. El trabajo de tesis propone 3 métodos de clustering alternativos al \ud \ud original para la textura, y un modelo de forma entrenado off-line con una función de ajuste robusta. Los métodos \ud \ud alternativos propuestos obtienen una amplia mejora tanto en la precisión del seguimiento como en la robustez de éste frente \ud \ud a giros de cabeza, oclusiones, gestos y cambios de iluminación. Los métodos propuestos tienen, además, una baja carga \ud \ud computacional, y son capaces de ejecutarse a velocidades en torno a 100 imágenes por segundo en un computador de sobremesa