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Biometric authentication systems are quite vulnerable to sophisticated spoofing attacks. To keep a good level of security, reliable spoofing detection tools are necessary, preferably implemented as software modules. The research in this field is very active, with local descriptors, based on the analysis of microtextural features, gaining more and more popularity, because of their excellent performance...
Holistic approaches of face recognition are not robust to illumination, scale, occlusion and age variations. Various studies indicate that the performance of holistic approaches degrades as the face database size increases. In this paper, we propose a user specific landmark geometry based approach that assigns weights to different geometrical distances according to their role in face recognition process...
Extracting robust and discriminatory features from images is a crucial task for infrared face recognition. For this reason, we have developed an infrared face recognition algorithm based on improved local features, which applies adaptive threshold quantization to encode the local directional energy. The conventional LBP-based feature as represented by the fix threshold encoding has limited distinguishing...
Today Human Computer Interaction (HCI) is one of the most important topics in machine vision and image processing fields. Through features can get beneficial information about the variety of emotions and gestures which are produced by the movements of facial major parts. In this paper we presented the technique of Pyramid Histogram of Oriented Gradient for feature extraction and compare it with gabor...
Many feature-based object detectors have shown that the use of gradient image information can be a very efficient way to describe the appearance of objects. Especially, the gradient sizes, directions and histograms are commonly used. In this area, the histogram of oriented gradients (HOG) is considered as the state-of-the-art method. The histograms and gradient orientations are used to encode the...
The Point-and-Shoot Face Recognition Challenge (PaSC) is a performance evaluation challenge including 1401 videos of 265 people acquired with handheld cameras and depicting people engaged in activities with non-frontal head pose. This report summarizes the results from a competition using this challenge problem. In the Video-to-video Experiment a person in a query video is recognized by comparing...
Based on fitting the Local Binary Patterns (LBP) histogram into the bag-of-words paradigm, we propose an LBP variant termed Principal Local Binary Patterns (PLBP) which are learned in an unsupervised way from the data. The learning problem turns out to be the same as the Principal Component Analysis (PCA) and thus can be solved very efficiently. Unlike the manually specified patterns in LBP which...
Spotting micro-expressions is a primary step for continuous emotion recognition from videos. Spotting in this context refers to automatically finding the temporal locations of the face-related events from a video sequence. Rapid facial movements mainly include micro-expressions and eye blinks. However, the role of eye blinks in expressing emotions is still controversial, and often they are considered...
LCVBP (Local Color Vector Binary Patterns) approach extracts multi-signal channel characteristics from color norm patterns and color angular patterns of a color image. As a result, feature dimension is higher and computational cost is greater. Hence, this paper presents a novel region-based LCVBP feature extraction method for face recognition. Firstly, we locate the feature points in a face image,...
The advent of near infrared imagery and it's applications in face recognition has instigated research in cross spectral (visible to near infrared) matching. Existing research has focused on extracting textural features including variants of histogram of oriented gradients. This paper focuses on studying the effectiveness of these features for cross spectral face recognition. On NIR-VIS-2.0 cross spectral...
Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classifier (SRC)...
The human interaction based framework for manipulable object categorization is proposed in this paper. In the proposed framework, co-occurrence and spatial relationship based features are developed to improve the categorization problem of the objects with high intra-class variation, deformable objects or the objects that are occluded. The descriptor in this framework is based on a co-occurrence of...
Glasses detection is one of attractive tasks in image processing since it increases the performance of face recognition systems. In this study, we aimed to detect the glasses on face images automatically. In order to do this, we trained a classifier with Labelled Faces in the Wild Home(LFW) dataset to decide whether a person wear glasses or not on face images. Before classification process, image...
In this paper, a feature combining method which can be used in gender classification has been proposed. This method is based on examinating the importance of the pixel regions on face images. In this study, after the analysing commonly used three feature extraction methods (Local binary patterns, discrete cosine transform, histogram of oriented gradients) dimension reduction is achieved via eliminating...
In this paper, we propose the effective similarity feature-based selection and classification algorithm to select similarity features on the training images and to classify face images in face recognition system. The experiments are conducted on The ORL Database of Faces, which consists of 400 images of 40 individuals. Two face recognition systems, one based on the histogram-based feature, and the...
In this paper, we investigate face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, three state of the art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are challenged against the IRIS-M3 multispectral face data base to evaluate their robustness...
Now a days, security is a major concern for any organization. It is very difficult to have enough faith in any person as far as security of the organization is concerned. Due to these reasons, face recognition gets popularity in the security domain. Many conventional methods are available to do the face recognition. In this paper, we have discussed few of them covering advantages, disadvantages and...
In this paper we are proposing a novel computer vision system that can recognize expression of pain in videos by analyzing facial features. Usually pain is reported and recorded manually and thus carry lot of subjectivity. Manual monitoring of pain makes difficult for the medical practitioners to respond quickly in critical situations. Thus, it is desirable to design such a system that can automate...
User identification and tracking are definitely the basic tasks in any human computer interaction (HCI) scenario. For these tasks we propose a multi-view approach utilizing multi-camera systems and audio processing systems. Face detectors and face recognizers are based on orientation histogram and eigenface techniques, and Mel Frequency Cepstral Coefficients (MFCC) are applied for speaker identification...
Spoofing attacks mainly include printing artifacts, electronic screens and ultra-realistic face masks or models. In this paper, we propose a component-based face coding approach for liveness detection. The proposed method consists of four steps: (1) locating the components of face; (2) coding the low-level features respectively for all the components; (3) deriving the high-level face representation...
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