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Facial recognition applications present a great interest in the area of computer vision, with various methods and approaches that provide impressive performance. However, not all studies investigate the possibilities of using proper feature extraction methods with efficient classifiers, for applications that facial expression is not required for detection. In this sense, we propose another facial...
In this paper, a new technique for constructing feature vector from DCT coefficients for gender classification has been presented. Firstly, images are divided into 8 × 8 sub images. DCT coefficients are calculated for each block in image. New technique is used for constructing the feature vector from DCT coefficients. Finally, SVM with Rbf kernel is used for classifying the images into male and female...
Thatcher effect or Thatcher illusion is a phenomenon where it becomes difficult to detect local feature changes in an upside down face, despite identical changes being obvious in an upright face. In the Thatcher illusion, in which the eyes and mouth are inverted relative to the rest of the face, looks grotesque when shown upright but not when inverted. Face double illusion is formed by replicating...
In this paper, we evaluate the effect of removing eyes or eyebrows from face image (no left eyebrow, no right eyebrow, no eyebrows, no left eye, no right eye, no eyes, no left eyebrow and no left eye, no right eyebrow and no right eye, no eyebrows and no eyes) on the performance of face recognition system based on Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Discrete Wavelet...
A face recognition system based on 2-D DCT features and pseudo-2D Hidden Markov Models is presented. The system achieves a recognition rate of 99.5% on the Olivetti Research Laboratory (ORL) face database. This recognition rate is much better than the recognition rate of a previous pseudo 2-D HMM approach. Only one single face out of the 200 available test faces was not correctly recognized. The superiority...
This paper deals with 4 different techniques for feature extraction of image. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. The Self-Organizing Map (SOM) Neural Network has been used for training of database and simulation of FR system. The developed algorithm for the face recognition system...
In this paper, a local appearance based face recognition algorithm is proposed. In the proposed algorithm local information is extracted using block-based discrete cosine transform. Obtained local features are combined both at the feature level and at the decision level. The performance of the proposed algorithm is tested on the Yale and CMU PIE face databases, and the obtained results show significant...
The goal of this paper is to present a critical comparison of existing classical techniques on recognition of human faces. This paper describes the four major classical face recognition techniques i.e., i) Principal Component Analysis (PCA), ii) Linear Discriminant Analysis (LDA), iii) Discrete Cosine Transform (DCT), and iv) Independent Component Analysis (ICA). Strong and weak features of these...
Appearance-based face recognition methods have achieved great success in face recognition, whereas these methods fail to work for face recognition from single sample per person (SSPP). However in the most real-world situations there is only one image per person available such as law enhancement, epassport and ID card identification. In this paper a novel mutimanifold learning techniqe called improved-DMMA...
This paper proposes an improved face recognition scheme using spectral domain features and a multi-layer classification mechanism. The efficiency of the new scheme, in correctly classifying images has been tested through an OpenCV implementation using a benchmark face database. The improved classification accuracy of the proposed method is evident from the experimental results.
This paper proposes a fast and efficient approach for face recognition under non uniform illumination variations. Robust Haar classifiers technique is used for face detection from an image. Since illumination variations lie in low frequency DCT coefficients, illumination variations is removed from detected face by rescaling down an appropriate number of low frequency DCT coefficients while still preserving...
In this paper, face images are analyzed in frequency domain and then classified based on the gender of the human subjects appearing in the image. Different images of the same gender are considered as an ensemble of inter-correlated signals and changes due to variation in faces are sparse with respect to the whole image. We exploit this sparsity using compressive sensing, which enables us to grossly...
This paper provides an integrated algorithm to deal with face recognition. It uses discrete cosine transform and principal component analysis to reduce dimensions and extract face features, and then trains and tests face images through the BP neural network classifier. It also seeks for other method such as the nearest neighbor classifier to have a comparison with BP neural network. Simulation result...
Gabor features have been extensively used for facial image analysis due to their powerful representation capabilities. This paper focuses on selecting and combining multiple Gabor classifiers that are trained on, for example, different scales and local regions. The system exploits curvature Gabor features in addition to conventional Gabor features. Final classifier is obtained by combining selected...
In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per-person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive...
In this work, we propose a framework for simultaneously detecting the presence of multiple facial action units using kernel partial least square regression (KPLS). This method has the advantage of being easily extensible to learn more face related labels, while at the same time being computationally efficient. We compare the approach to linear and non-linear support vector machines (SVM) and evaluate...
In this paper, we present a common framework for realtime action unit detection and emotion recognition that we have developed for the emotion recognition and action unit detection sub-challenges of the FG 2011 Facial Expression Recognition and Analysis Challenge. For these tasks we employed a local appearance-based face representation approach using discrete cosine transform, which has been shown...
In this paper, we propose a novel learning-based face hallucination framework built in the DCT domain, which can produce a high-resolution face image from a single low-resolution one. The problem is formulated as inferring the DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain. Our study shows that DC coefficients can be estimated fairly accurately by simple...
This paper presents an appropriate solution for low resolution faces recognition problem, using combination of diverse classifiers. We investigate our model based on extracting important features from low resolution images using three well known feature extractors; PCA, DCT and FFT, assigning MLP classifiers to each feature extractor and combining the votes of MLP classifiers using fusion of experts...
Locality preserving projection (LPP) is a promising manifold learning approach for dimensionality reduction. However, it often encounters small sample size (3S) problem in face recognition tasks. To overcome this limitation, this paper proposes a discrete sine transform (DST) feature extraction approach and develops a DST-feature based LPP algorithm for face recognition. The proposed method has been...
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