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In this paper, we propose a discriminative keypoint selection-based 3D face recognition method that is superior to prevalent techniques in terms of both computational complexity and performance. We use the average face model (AFM) for face registration to efficiently locate the axis of symmetry in the rotated face mesh and recover a full frontal face from a 3D face model commonly corrupted due to...
In this study, an SVM-based system is proposed for the classification of facial expressions that are represented in 3D. Distance based features are used as a feature vector, which are determined by the distances between the different key points on the image. Study was conducted on a subset (Happy, sadness, surprise) of Bosphorus 3D Face Database. 9 different fiducial points are used to calculate a...
Representation-based classifiers (RCs) including sparse RC (SRC) have attracted intensive interest in pattern recognition in recent years. In our previous work, we have proposed a general framework called atomic representation-based classifier (ARC) including many popular RCs as special cases. Despite the empirical success, ARC and conventional RCs utilize the mean square error (MSE) criterion and...
The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bilinear CNN (B-CNN), which has shown dramatic performance gains on certain fine-grained recognition problems [15]. We apply this new CNN to the challenging new face recognition benchmark, the IARPA Janus Benchmark A (IJB-A)...
We have applied Latent Topic Models to facial expression recognition. We showed that the latent topic learned from a topic model is very similar to the Action Units defined by psychologists in the Facial Action Coding Systems (FACS). Furthermore, we noted that the topics thus obtained may be correlated with each other, and we tried to model this by the correlated topic model (CTM). Preliminary results...
This paper derives a child and adulthood classification technique by integrating the statistical and structural approaches. The structural approaches are derived on a 3 × 3 window based on Local binary pattern (LBP) approach. The proposed approach divides the LBP in to two structural patterns. The present paper derives two distinct patterns called Left Diagonal (LD) and Right Diagonal (RD) LBP's....
In order to handle pose variation problem in face recognition, Generic Elastic Models (GEMs) was proposed as a low computational and efficient 3D face modeling method, which generates 3D face model from single frontal face image by elastically deforming a generic 3D depth map based on 2D observations of the input face image. In this paper, we extend GEMs to Multi-Depth GEMs (MD-GEMs) by utilizing...
In this paper, we present a novel approach for recognition of human faces using Markov Random Fields (MRF) and Bayesian models. We examine the relationship between feature vectors in a close proximity system. The feature vectors are coefficients of the 2D Gabor Wavelet Transform (DWGT). The MRF is implemented to match the constraint configurations between the feature vectors. The MRFs posterior probability...
Researchers have shown that the changes in face features due to plastic surgery can be modeled as a covariate that reduces the ability of algorithms to recognize a person’s identity. Traditional dictionary learning methods learn a sparse representation using $l_{0}$ and $l_{1}$ norms that are computationally expensive. This paper presents a multiple projective dictionary learning (MPDL) framework...
In this paper, we propose efficient face recognition based on Grayscale Arranging Pairs (GAP) feature. GAP is a robust holistic feature considering the intensity relationships about all of pixels. Therefore, it has good performance for face recognition. However, GAP feature consider all of pixels and it takes high computational time. In order to reduce computational time, we uses GAP feature in terms...
Face is a highly utilized biometric, and 3D modality is preferred due to better handling of variations such as pose and illumination. However, occlusions covering the face alter the 3D surface and degrade the recognition performance. To improve recognition rates, the occluded parts should be detected prior to any surface comparison. In this paper, we consider two different occlusion detection approaches:...
Facial expressions are one of the most important elements for our social interaction. Automatic processing and recognition of facial expressions is hence one of the core areas in computer vision, computer graphics, and social signal processing. Conditional Random Fields (CRFs) and their extensions are widely used for recognizing facial expressions. Most research in this area, however, is done either...
In this paper, we propose a new collaborative reconstruction-based manifold-manifold distance (CRMMD) method for face recognition with image sets, where each gallery and probe sample is a set of face images captured from varying poses, illuminations and expressions. Given each face image set, we first model it as a nonlinear manifold and then the recognition task is converted as a manifold-manifold...
Facial expression is one of the main issues of face recognition in uncontrolled environments. In this paper, we apply the probabilistic linear discriminant analysis (PLDA) method to recognize faces across expressions. Several PLDA approaches are tested and cross-evaluated on the Cohn-Kanade and JAFFE databases. With less samples per gallery subject, high recognition rates comparable to previous works...
Detection and location of the face as well as extraction of facial features from images is an important stage for numerous facial image interpretation tasks. Detection of facial feature points, such as corners of eyes, lip corners, nostrils from the images are crucial. In this paper a method for autormatic facial feature point detection in image sequences, is introduced. The method uses image normalization,...
Improved Pseudo-Zernike Moment (PZM) and artificial neural network (ANN) was combined within the hybrid architecture for face recognition. Improved PZM was used to extract face feature, and encoded to form the input vector sending to ANN. Experimental results demonstrate the present approach taking advantage of ANN, basically eliminates the effects of the change of face scale and rotation, and has...
This paper shows how “Body Motion Signature Analysis” - a new “soft-biometrics” technique - can be used for identity verification. It is able to extract motion features from the upper body of people and estimates so called “super-features” for input to a classifier. We demonstrate how this new technique can be used to identify people just based on their motion, or it can be used to significantly improve...
Computerised facial aging estimation, which has the potential for many applications in human-computer interactions, has been investigated by many computer vision researchers in recent years. In this paper, a feature-based discriminant subspace is proposed to extract more discriminating and robust representations for aging estimation. After aligning all the faces by a piece-wise affine transform, orthogonal...
The performance (in term of error rate) of biometric systems can be improved by combining them. Multiple fusion techniques can be applied from classical logical operations to more complex ones based on score fusion. In this paper, we use a genetic algorithm to learn the parameters of different multibiometrics fusion functions. We are interested in biometric systems usable on any computer (they do...
E-Learning is a kind of study system. In this system we can teach and learn through the Internet and other digitized media. Affective Computing is related to, arises from or deliberately influences emotions. It tries to construct a kind of computer system which can recognize, synthesize and understand the human emotions to make intelligent, smart, friendly reaction. In this paper we combine the e-Learning...
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