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

A NOVEL FACE DETECTION AND TRACKING ALGORITHM IN REAL- TIME VIDEO SEQUENCES

Face detection is a image processing technology that determines the location and size of human faces in digital images or video. This module precedes face recognition systems that plays an important role in applications such as video surveillance, human computer interaction and so on. This proposed work focuses mainly on multiple face detection technique, taking into account the variations in digital images or video such as face pose, appearances and illumination. The work is based on skin color model in YCbCr and HSV color space. First stage of this proposed method is to develop a skin color model and then applying the skin color segmentation in order to specify all skin regions in an image. Secondly, a template matching is done to assure that the segmented image does not contain any non-facial part. This algorithm works to be robust and efficient

A hybrid face detection system using combination of appearance-based and feature-based methods

Human face detection is preliminary required step of face recognition systems as well as a very important task in many applications, such as security access control systems, video surveillance, human computer interface and image database management. This paper intends to combine Viola and Jones face detection method with a color-based method to propose an improved face detection method. Experimental results show that our method efficiently decreased false positive rate and subsequently increased accuracy of the face detection system especially in complex background images. Also our proposed method considerably increased face detection speed

A simple and efficient eye detection method in color images

International audienceIn this paper we propose a simple and efficient eye detection method for face detection tasks in color images. The algorithm first detects face regions in the image using a skin color model in the normalized RGB color space. Then, eye candidates are extracted within these regions. Finally, using the anthrophological characteristics of human eyes, the pairs of eye regions are selected. The proposed method is simple and fast, since it needs no template matching step for face verification. It is robust because it can deals with face rotation. Experimental results show the validity of our approach, a correct eye detection rate of 98.4% is achieved using a subset of the AR face database

Face detection in profile views using fast discrete curvelet transform (FDCT) and support vector machine (SVM)

Human face detection is an indispensable component in face processing applications, including automatic face recognition, security surveillance, facial expression recognition, and the like. This paper presents a profile face detection algorithm based on curvelet features, as curvelet transform offers good directional representation and can capture edge information in human face from different angles. First, a simple skin color segmentation scheme based on HSV (Hue - Saturation - Value) and YCgCr (luminance - green chrominance - red chrominance) color models is used to extract skin blocks. The segmentation scheme utilizes only the S and CgCr components, and is therefore luminance independent. Features extracted from three frequency bands from curvelet decomposition are used to detect face in each block. A support vector machine (SVM) classifier is trained for the classification task. In the performance test, the results showed that the proposed algorithm can detect profile faces in color images with good detection rate and low misdetection rate

Robust human face detection in complex color images

We propose in this paper a model based technique for the detection of human faces from rich still color images. Traditionally, color images are represented in the RGB color space. RGB space, however, is not only a 3-dimensional space but also includes brightness or luminance which is not a reliable criterion for skin separation. To avoid the effect of luminance, we propose to work in the chromatic or pure color space. Using such space, a Gaussian model for the skin color pixels is developed and a skin likelihood image is obtained. Such image is then transformed into a binary image using adaptive thresholding. Finally, bright regions satisfying certain "facial" properties are obtained followed by a template matching stage. The method presented here is shown to provide robust detection under different environments and found to achieve very satisfactory results when compared to traditional "mug shot" based approaches

Robust human face detection in complex color images

We propose in this paper a model based technique for the detection of human faces from rich still color images. Traditionally, color images are represented in the RGB color space. RGB space, however, is not only a 3-dimensional space but also includes brightness or luminance which is not a reliable criterion for skin separation. To avoid the effect of luminance, we propose to work in the chromatic or pure color space. Using such space, a Gaussian model for the skin color pixels is developed and a skin likelihood image is obtained. Such image is then transformed into a binary image using adaptive thresholding. Finally, bright regions satisfying certain "facial" properties are obtained followed by a template matching stage. The method presented here is shown to provide robust detection under different environments and found to achieve very satisfactory results when compared to traditional "mug shot" based approaches

Pengenalan Wajah Menggunakan Metode Fisherface

This paper describes human identification using fisherface method to identify someone. The output is whether recognized or not an input image as an individual in the database. There are four main stages for this method, mainly face detection, PCA (Principal Component Analysis) calculation, FLD (Fisher's Linear Analysis) calculation and classification stage. In face detection stage, color thresholding is used to segment pixels that contain skin color. PCA calculation and FLD calculation stages are used to form a set of fisherfaces from a training set or database that will be used. All face images can be reconstructed from the combination of fisherfaces with different weights for each face image. The last stage, classification stage, is to identify the input image by comparing the weight of fisherface required to reconstruct the input face towards face images in the training set. The weight calculation is done by using Euclidian distance method. The simulations are done for 66 input images and the successful recognition rate is about 81.82%