Skin Colour Segmentation using Fintte Bivariate Pearsonian Type-IV a Mixture Model

The human computer interaction with respect to skin colour is an important area of research due to its ready applications in several areas like face recognition, surveillance, image retrievals, identification, gesture analysis, human tracking etc.  For efficient skin colour segmentation statistical modeling is a prime desiderata.  In general skin colour segment is done based on Gaussian mixture model.  Due to the limitations on GMM like symmetric and mesokurtic nature the accuracy of the skin colour segmentation is affected.  To improve the accuracy of the skin colour segmentation system, In this paper the skin colour is modeled by a finite bivariate Pearsonian type-IVa mixture distribution under HSI colour space of the image.  The model parameters are estimated by EM algorithm.  Using the Bayesian frame the segmentation algorithm is proposed.  Through experimentation it is observed that the proposed skin colour segmentation algorithm perform better with respect to the segmentation quality metrics like PRI, GCE and VOI.  The ROC curves plotted for the system also revealed that the developed algorithm segment pixels in the image more efficiently. Keywords: Skin colour segmentation, HSI colour space, Bivariate Pearson type IVa mixture model, Image segmentation metrics

Segmentation of the face and hands in sign language video sequences using color and motion cues

Copyright © 2004 IEEEWe present a hand and face segmentation methodology using color and motion cues for the content-based representation of sign language video sequences. The methodology consists of three stages: skin-color segmentation; change detection; face and hand segmentation mask generation. In skin-color segmentation, a universal color-model is derived and image pixels are classified as skin or nonskin based on their Mahalanobis distance. We derive a segmentation threshold for the classifier. The aim of change detection is to localize moving objects in a video sequences. The change detection technique is based on the F test and block-based motion estimation. Finally, the results from skin-color segmentation and change detection are analyzed to segment the face and hands. The performance of the algorithm is illustrated by simulations carried out on standard test sequences.Nariman Habili, Cheng Chew Lim, and Alireza Moin

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Class Separation Improvements in Pixel Classification Using Colour Injection

This paper presents an improvement in the colour image segmentation in the Hue Saturation (HS) sub-space. The authors propose to inject (add) a colour vector in the Red Green Blue (RGB) space to increase the class separation in the HS plane. The goal of the work is the development of an algorithm to obtain the optimal colour vector for injection that maximizes the separation between the classes in the HS plane. The chromatic Chrominace-1 Chrominance-2 sub-space (of the Luminance Chrominace-1 Chrominance-2 (YC1C2) space) is used to obtain the optimal vector to add. The proposal is applied on each frame of a colour image sequence in real-time. It has been tested in applications with reduced contrast between the colours of the background and the object, and particularly when the size of the object is very small in comparison with the size of the captured scene. Numerous tests have confirmed that this proposal improves the segmentation process, considerably reducing the effects of the variation of the light intensity of the scene. Several tests have been made in skin segmentation in applications for sign language recognition via computer vision, where an accurate segmentation of hands and face is required

FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION

This thesis consists of three parts: face localization, features selection and classification process. Three methods were proposed to locate the face region in the input image. Two of them based on pattern (template) Matching Approach, and the other based on clustering approach. Five datasets of faces namely: YALE database, MIT-CBCL database, Indian database, BioID database and Caltech database were used to evaluate the proposed methods. For the first method, the template image is prepared previously by using a set of faces. Later, the input image is enhanced by applying n-means kernel to decrease the image noise. Then Normalized Correlation (NC) is used to measure the correlation coefficients between the template image and the input image regions. For the second method, instead of using n-means kernel, an optimized metrics are used to measure the difference between the template image and the input image regions. In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. The above-mentioned three methods showed accuracy of localization between 98% and 100% comparing with the existed methods. In the second part of the thesis, Discrete Wavelet Transform (DWT) utilized to transform the input image into number of wavelet coefficients. Then, the coefficients of weak statistical energy less than certain threshold were removed, and resulted in decreasing the primary wavelet coefficients number up to 98% out of the total coefficients. Later, only 40% statistical features were extracted from the hight energy features by using the variance modified metric. During the experimental (ORL) Dataset was used to test the proposed statistical method. Finally, Cluster-K-Nearest Neighbor (C-K-NN) was proposed to classify the input face based on the training faces images. The results showed a significant improvement of 99.39% in the ORL dataset and 100% in the Face94 dataset classification accuracy. Moreover, a new metrics were introduced to quantify the exactness of classification and some errors of the classification can be corrected. All the above experiments were implemented in MATLAB environment

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

FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION

This thesis consists of three parts: face localization, features selection and classification process. Three methods were proposed to locate the face region in the input image. Two of them based on pattern (template) Matching Approach, and the other based on clustering approach. Five datasets of faces namely: YALE database, MIT-CBCL database, Indian database, BioID database and Caltech database were used to evaluate the proposed methods. For the first method, the template image is prepared previously by using a set of faces. Later, the input image is enhanced by applying n-means kernel to decrease the image noise. Then Normalized Correlation (NC) is used to measure the correlation coefficients between the template image and the input image regions. For the second method, instead of using n-means kernel, an optimized metrics are used to measure the difference between the template image and the input image regions. In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. The above-mentioned three methods showed accuracy of localization between 98% and 100% comparing with the existed methods. In the second part of the thesis, Discrete Wavelet Transform (DWT) utilized to transform the input image into number of wavelet coefficients. Then, the coefficients of weak statistical energy less than certain threshold were removed, and resulted in decreasing the primary wavelet coefficients number up to 98% out of the total coefficients. Later, only 40% statistical features were extracted from the hight energy features by using the variance modified metric. During the experimental (ORL) Dataset was used to test the proposed statistical method. Finally, Cluster-K-Nearest Neighbor (C-K-NN) was proposed to classify the input face based on the training faces images. The results showed a significant improvement of 99.39% in the ORL dataset and 100% in the Face94 dataset classification accuracy. Moreover, a new metrics were introduced to quantify the exactness of classification and some errors of the classification can be corrected. All the above experiments were implemented in MATLAB environment