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Face recognition has been receiving continuous academic and commercial attention for the last decades. In this paper, we construct two face recognition systems adopting SVM and Adaboost as the classifiers with fast PCA for facial feature representation. The detailed discussions about algorithm realization are given. Comparison between the two systems and analysis of them are provided through several...
Face recognition under varying illumination and dimensionality reduction has been a key problem in the field of Computer Vision. An extension of Principal Component Analysis (PCA) called Independent Component Analysis (ICA) has been utilised in this paper as a feature extraction technique. In the proposed approach three feature selection techniques have been investigated namely Adaboost, Gentle-Adaboost...
This paper presents a new face gender recognition scheme by enjoying the benefit from the dot diffusion among weak classifiers in recognition phase for a low resolution and non-aligned thumbnail image. The main problem of the former Adaboost approaches is that each weak classifier simply offers a binary decision, which fails to compensate the decision error by diffusing it to the rest weak classifiers...
Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features...
The paper presents a novel approach for solving face recognition problem. We combine Gabor filters and Principal Component Analysis (PCA) to extract feature vectors; then we apply Support Vector Machine (SVM), the most powerful discriminative method, and AdaBoost, a meta-algorithm, for classification. Experiments for the proposed method have been conducted on two public face database AT&T and...
In recent years, LBP feature based SVM detector and Haar feature based cascade detector are the two types of efficient detectors for face detection. In this paper, we proposed to improve the performance on Haar feature based cascade detector. First, we describe a new feature for cascade detector. The feature is called Separate Haar Feature. Secondly, we describe a new decision algorithm in cascade...
We introduce a facial expression recognition method, which incorporates a weight to the Local Binary Pattern (LBP), and generates solid expression features. Furthermore, we use Adaboost to select a small set of prominent features, which is used by the Support Vector Machine (SVM) to classify facial expressions efficiently. Experimental results demonstrate that our method outperforms the state-of-the-art...
In this paper, we propose a pedestrian analysis solution helpful for adaptive content delivery and interest measurement for outdoor advertisement displays. The proposed system has built-in camera on the top panel of such displays which capture the real time viewers' frames. The captured frames have been analyzed for detection of faces using Viola-Jones algorithm. The detected faces have been processed...
The Discriminative Filtering technique performs pattern recognition using a two-dimensional filter. It has a closed-form design, based on the pattern and the statistics of the image set. Here, we investigate the use of Discriminative Filtering for detecting fiducial points in human faces. We show that designing discriminative filters for the principal components increases robustness. The method is...
We investigate the problem of facial expression recognition using 3D face data. Our approach is based on local shape analysis of several relevant regions of a given face scan. These regions or patches from facial surfaces are extracted and represented by sets of closed curves. A Riemannian framework is used to derive the shape analysis of the extracted patches. The applied framework permits to calculate...
Human face recognition plays an important role in application such as human computer interface, video surveillance and face image database management. Automatic face recognition is a very challenging technique. Up to date, there are still substantial challenging problems which remain to be solved. This paper presents an automatic face recognition solution. At first, the AdaBoost method is used for...
Based on complexion-viscera diagram in TCM (Traditional Chinese Medicine), a method for complexion recognition was proposed for the first time. Firstly, the sampling environment was standardized, and 68 common facial feature points were localized by AdaBoost and ASM algorithm. Second, the complexion features in LAB color space were extracted from 15 diagnostic feature points on complexion-viscera...
All family members resemble each other in different ways which is recognizable by our brain. In this paper, we have developed family classification using AdaBoost, Support Vector Machines and K-Nearest Neighbor classifiers with different patches of training data. In some cases family classification involve unseen data classification in which the classifiers' performance drop significantly. Therefore...
The traditional E-learning System is to achieve self-learning function of distance. But in the learning process, the learner's emotional information can not be extracted effectively use that the study results is not ideal. Teaching system in the network to increase emotional recognition, it is a personalized Web-based Education is an important direction for the development. This paper using Adaboost...
In this paper, we have proposed a new face detection method which combines the AdaBoost algorithm with skin color information and support vector machine (SVM). First, a cascade classifier based on AdaBoost is used to detect faces in images. Due to noise and illumination changes some nonfaces might be detected too, therefore we have used a skin color model in the YCbCr color space to remove some of...
Different facial expressions are related to a small set of muscles and limited ranges of motions. In this paper we propose an automatic facial expression recognition system, different from other automatic methods in both face detection and feature extraction. In system the facial expressions identify itself in video sequences. First, the differences between neutral and emotional states are detected...
Recognizing smiles is of much importance for detecting happy moods. Gabor features are conventionally widely applied to facial expression recognition, but the number of Gabor features is usually too large. We proposed to use pyramid histogram of oriented gradients (PHOG) as the features extracted for smile recognition in this paper. The comparisons between the PHOG and Gabor features using a publicly...
Effects of driver's states adaptive driving support systems is highly expected for the prevention of traffic accidents. In order to create this constituent technology, detecting driver's psychosomatic states which occurs just before a traffic accident is essential. Therefore driver's distraction is thought as one of important factors. This study focused on detecting driver's cognitive distraction,...
We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear...
Eye state detection in facial image is a significant issue in face recognition, human-computer interface and driver fatigue monitoring system. In this paper, we first located the eye region in the upper area of the face region with AbaBoost algorithm. The linear predictor error distribution of wavelet coefficients was proposed as the statistics model to distinguish the eye states. We collected statistics...
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