A simple and efficient face detection algorithm for video database applications

The objective of this work is to provide a simple and yet efficient tool to detect human faces in video sequences. This information can be very useful for many applications such as video indexing and video browsing. In particular the paper focuses on the significant improvements made to our face detection algorithm presented by Albiol, Bouman and Delp (see IEEE Int. Conference on Image Processing, Kobe, Japan, 1999). Specifically, a novel approach to retrieve skin-like homogeneous regions is presented, which is later used to retrieve face images. Good results have been obtained for a large variety of video sequences.Peer ReviewedPostprint (published version

Detecção facial: autofaces versus antifaces

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.No presente trabalho, é desenvolvido um estudo comparativo entre duas técnicas de detecção facial baseadas em projeções vetoriais: Autofaces e Antifaces. O método de Autofaces tem sido significativamente estudado nos últimos anos, enquanto o de Antifaces é ainda considerado o estado-da-arte para a detecção de objetos. Ambos os métodos são descritos de forma detalhada e, para o método de Antifaces, é proposto um procedimento que permite obter os detectores subótimos. Ambos os métodos são avaliados em condições idênticas de teste. Tais avaliações consideram detecções de características faciais, de objetos tridimensionais e de uma face específica, vista de um ângulo frontal. Finalmente, é feita uma análise de sensibilidade dos métodos ao ruído branco Gaussiano aditivo, a distorções no foco e a alterações na cena em que se apresenta o objeto de interesse. Através dos resultados obtidos, é possível constatar que, no método de Antifaces, os critérios para a determinação de algumas variáveis de projeto não estão ainda bem estabelecidos. Além disso, esse método apresenta alta seletividade durante o processo de detecção. O método de Autofaces possui maior capacidade de generalização e menor sensibilidade à adição de ruído, distorções no foco e alterações na cena

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2

Modelo de processamento de imagem, com múltiplas fontes de aquisição, para manipulação aplicada à domótica

Este trabalho foca-se em modelos de processamento de imagem para utilização na visão por computador. Modelos de processamento de imagem com multi-aquisição e/ou em multi-perspectiva, para um conhecimento do meio circundante, com possibilidade de comando e controlo na área da domótica e/ou robótica móvel. Os algoritmos desenvolvidos têm a capacidade de serem implementados em blocos de software ou hardware, de forma independente (autónomos), ou integrados como componentes de um sistema mais complexo. O desenvolvimento dos algoritmos privilegiou o seu elevado desempenho, constrangido pela minimização da carga computacional. Nos modelos de processamento de imagem desenvolvidos foram focados 4 tópicos fundamentais de investigação: a) detecção de movimento de objectos e seres humano em ambiente não controlado; b) detecção da face humana, a ser usada como variável de controlo (entre outras aplicações); c) capacidade de utilização de multi-fontes de aquisição e processamento de imagem, com diferentes condições de iluminação não controladas, integradas num sistema complexo com diversas topologias; d) capacidade de funcionamento de forma autónoma ou em rede distribuída, apenas comunicando resultados finais, ou integrados modularmente na solução final de sistemas complexos de aquisição de imagem. A implementação laboratorial, com teste em protótipos, foi ferramenta decisiva no melhoramento de todos os algoritmos desenvolvidos neste trabalho; IMAGE PROCESSING MODELS, WITH MULTIPLE ACQUISITION SOURCES, FOR MANIPULATION IN DOMOTICS Abstract: This work focuses on image processing models for computer vision. Image processing models with multi-acquisition and/or multi-perspective models were developed to acquire knowledge over the surrounding environment, allowing system control in the field of domotics and/or mobile robotics. The developed algorithms have the capacity to be implemented in software or hardware blocks, independently (autonomous), or integrated as a component in more complex systems. The development of the algorithms was focused on high performance constrained by the computational burden minimization. In the developed image processing models it were addressed 4 main research topics: a) movement detection of objects and human beings in an uncontrolled environment; b) detection of the human face to be used as a control variable (among other applications); c) possibility of using multi-sources of acquisition and image processing, with different uncontrolled lighting conditions, integrated into a complex system with different topologies; d) ability to work as an autonomous entity or as a node integrated on a distributed network, only transmitting final results, or integrated as a link in a complex image processing system. The laboratorial implementation, with prototype tests, was the main tool for the improvement of all developed algorithms, discussed in the present wor

A simple and efficient face detection algorithm for video database applications

The objective of this work is to provide a simple and yet efficient tool to detect human faces in video sequences. This information can be very useful for many applications such as video indexing and video browsing. In particular the paper focuses on the significant improvements made to our face detection algorithm presented by Albiol, Bouman and Delp (see IEEE Int. Conference on Image Processing, Kobe, Japan, 1999). Specifically, a novel approach to retrieve skin-like homogeneous regions is presented, which is later used to retrieve face images. Good results have been obtained for a large variety of video sequences.Peer Reviewe