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Content related to "face-recognition"

(1) Statistical Classification of Skin Color Pixels from MPEG Videos

Detection and classification of skin regions plays important roles in many image processing and vision applications. In this paper, we present a statistical approach for fast skin detection in MPEG-compressed videos. Firstly, conditional probabilities of skin and non-skin are extracted from manual marked training images. Then, candidate skin pixels are identified using the Bayesian maximum a posteriori decision rule. An optimal threshold is then obtained by analysis of probability error on the basis of the likelihood ratio histogram of skin and nonskin pixels. Experiments from sequences with varying illuminations have demonstrated that effectiveness of our approach.

(1) Recognition of JPEG Compressed Face Images Based on AdaBoost

This paper presents an advanced face recognition system based on AdaBoost algorithm in the JPEG compressed domain. First, the dimensionality is reduced by truncating some of the block-based DCT coefficients and the nonuniform illumination variations are alleviated by discarding the DC coefficient of each block. Next, an improved AdaBoost.M2 algorithm which uses Euclidean Distance(ED) to eliminate non-effective weak classifiers is proposed to select most discriminative DCT features from the truncated DCT coefficient vectors. At last, the LDA is used as the final classifier. Experiments on Yale face databases show that the proposed approach is superior to other methods in terms of recognition accuracy, efficiency, and illumination robustness.

(1) Face Detection based on Skin Color in Image by Neural Networks

Face detection is one of the challenging problems in the image processing. A novel face detection system is prsented in this paper. The approach relies on skin-based color features extracted from two dimentional Discreate Cosine Transfer (DCT) and neural networks, which can be used to detect faces by using skin color from DCT coefficient of Cb and Cr feature vectors. This system contains the skin color which is the main feature of faces for detection, and then the skin face candidate is examined by using the neural networks, which learn from the feature of faces to classify whether the original image includes a face or not. The processing is based on normalization and Discreate Cosin Transfer. Finally the classification based on neural networks approch. The expriment results on upright frontal color face images from the internt show an exellent detection rate.

(1) Robustness Analysis on Facial Image Description in DCT Domain

In this letter, we report a DCT domain analysis of facial images to reveal that, when certain number of DCT coefficients are removed, the corresponding facial image description by the remaining DCT coefficients becomes robust to lighting changes and scale variations. Such nice properties would be very useful for applications of face recognition, video object tracking, object segmentation and visual content processing.

(1) Face Detection based Neural Networks using Robust Skin Color Segmentation

This paper proposes a robust schema for face detection system via Gaussian mixture model to segment image based on skin color. After skin and non skin face candidates’ selection, features are extracted directly from discrete cosine transform (DCT) coefficients computed from these candidates. Moreover, the back-propagation neural networks are used to train and classify faces based on DCT feature coefficients in Cb and Cr color spaces. This schema utilizes the skin color information, which is the main feature of face detection. DCT feature values of faces, representing the data set of skin/non-skin face candidates obtained from Gaussian mixture model are fed into the back-propagation neural networks to classify whether the original image includes a face or not. Experimental results shows that the proposed schema is reliable for face detection, and pattern features are detected and classified accurately by the backpropagation neural networks.

(1) D5.4 Report on evaluation of methods

This document reports on the first evaluation of tools developed in the LIVE project for manual, semiautomatic and automatic annotation and extraction of knowledge in work package 5. We start this report with findings on the international TRECVID 2007 evaluation of LIVE tools for automatic shot boundary classification. The compressed domain shot boundary detector developed in the LIVE project showed the third best recognition performance of all 15 participating research groups in this competition. Despite the excellent results, the generalization of the performance from news and documentary data used in TRECVID 2007 to more difficult sports data produced by the LIVE streams of Olympia 2008 remains difficult. Only further evaluations on labelled data stemming from Olympia 2004 and the upcoming Olympia 2008 event will show how suitable the developed technology is for extracting information automatically from sports broadcasts – a domain, for which neither standard international benchmarks nor any international competition exist. The detection of gradual transitions in sports video must still be considered unsolved and need further research. However, the evaluation results of TRECVID show the potential of the developed technology and their maturity. The next section of this document deals with the performance of different face recognition methods which are developed in the LIVE project to identify athletes and other important persons in the video stream automatically. We measure the performance in rather controlled optimal situations, benchmarked on the Bochum gallery, but also on a “worse-case” gallery with rather mixed content. The result is promising but uncontrolled environment and incorrect feature correspondence lead to poor results – especially if more advanced P2D-HMMs face recognition technology is applied. Hence, component face detectors have been developed in the project in order to improve the correspondence search in pose estimation before any identification can be performed. We report in this document on the performance of several face component detectors for eyes, nose and mouth locations developed in the course of the project to improve face pose estimation and recognition. Despite the fact that the performance of individual face component detectors is quite high when evaluated on a test set stemming from the same database, generalization of the facial recognition algorithms to other more uncontrolled galleries remains a challenge. However, as the integration of the face component detectors in the face recognition framework is still lacking, no sound evaluation can be performed. We will report in an upcoming report D 5.7 on the results of our research and how the different algorithms perform on Olympia 2008 sports data during the field trial.

(1) An efficient face image retrieval through DCT features

This paper proposes a new simple method of DCT feature extraction that utilize to accelerate the speed and decrease storage needed in image retrieving process by the aim of direct content access and extraction from JPEG compressed domain. Our method extracts the average of some DCT block coefficients. This method needs only a vector of six coefficients per block over the whole image blocks In our retrieval system, for simplicity, an image of both query and database are normalized and resized from the original database based on the cantered position of the eyes, the normalized image equally divided into non overlapping 8X8 block pixel Therefore, each of which are associated with a feature vector derived directly from discrete cosine transform DCT. Users can select any query as the main theme of the query image. The retrieval images is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the Euclidean distance. The experimental results show that our approach is easy to identify main objects and reduce the influence of background in the image, and thus improve the performance of image retrieval.

(1) Skin Detection from Different Color Spaces for Model-based Face Detection

Skin and face detection has many important applications in intelligent human-machine interfaces, reliable video surveillance and visual understanding of human activities. In this paper, we propose an efficient and effective method for frontal-view face detection based on skin detection and knowledge-based modeling. Firstly, skin pixels are modeled by using supervised training, and boundary conditions are then extracted for skin segmentation. Faces are further detected by shape filtering and knowledge-based modeling. Skin results from different color spaces are compared. In addition, experimental results have demonstrated our method robust in successful detection of skin and face re-gions even with variant lighting conditions and poses.