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In this paper a framework is presented to deals with various aspects of face recognition like illumination, rotation and scaling. The proposed framework consists of three parts. In the first part Gabor filter is used over the thermal faces at different scales, locations, and orientations. In second part, the fixed point algorithm Fast ICA have been used over the Gabor filtered images to represent...
For which low frequency discrete cosine transform (DCT) coefficients retransforming based on contrast limiting adaptive histogram equalization (CLAHE) is proposed. Firstly, original images are divided into several non-overlapping blocks and CLAHE is used to do local contrast stretching so as to reduce noise. Then, illustration variation of face image is removed by reducing suit numbers of low frequency...
In clutter background, the performance of tracking target can be influenced by the factors such as illumination, camera angle. Meanwhile the target can be occluded by some obstacles in the background or be occluded by the target itself. To solve those problems, a multi-window tracking method is proposed, which represents the tracking target with several windows that each one corresponds with a tracker...
This research aims at studying the recognition accuracy and execution time that are affected by different dimensionality reduction methods applied to the biometric image data. We comparatively study the fingerprint, face images, and handwritten signature data that are pre-processed with the two statistical based dimensionality reduction methods: principal component analysis (PCA) and linear discriminant...
This paper presents a novel method to recognize subtle emotions based on optical strain magnitude feature extraction from the temporal point of view. The common way that subtle emotions are exhibited by a person is in the form of visually observed micro-expressions, which usually occur only over a brief period of time. Optical strain allows small deformations on the face to be computed between successive...
Biometric based identifications are widely adopted for personnel identification. The unimodal recognition systems currently suffer from noisy data, spoofing attacks, biometric sensor data quality and many more. Robust personnel recognition considering multimodal biometric traits can be achieved. This paper introduces the Multimodal Personnel Authentication using Finger vein and Face Images (MPAFFI)...
Biometric systems accurately recognise/authenticate an individual to access his confidential data/accounts. When multiple traits are fused together at feature/ score/ decision level, it results into highly accurate multimodal systems. This system improvise rate of recognizing an individual. Multiple biometric traits cannot be cloned simultaneously and hence it is highly secured system. The match scores...
With the rapid accumulation of high dimensional data, dimensionality reduction plays a more and more important role in practical data processing and analysing tasks. This paper studies semi-supervised dimensionality reduction using pair wise constraints. In this setting, domain knowledge is given in the form of pair wise constraints, which specifies whether a pair of instances belong to the same class...
Sparse representation for classification (SRC) has attracted much attention in recent years. It usually performs well under the following assumptions. The first assumption is that each class has sufficient training samples. In other words, SRC is not good at dealing with the undersampled problem, i.e., each class has few training samples, even single sample. The second one is that the sample vectors...
Palm print is an emerging type of biometric that attracts researchers in biometrics area. As compared to the other biometric traits such as face, fingerprint and iris, the image quality of a fingerprint is robust with more information can be employed even though it is in low resolution. A new approach in feature extraction called evolution of kernel principal component analysis (Evo-KPCA) was proposed...
Local appearance descriptors are widely used on facial emotion recognition tasks. With these descriptors, image filters, such as Gabor wavelet or local binary patterns (LBP) are applied on the whole or specific regions of the face to extract facial appearance changes. But it is also clear that beside feature descriptor; choice of suitable learning method that integrates feature novelty is vital. The...
Facial physical appearance normally have several variations which occurred due to changes in expression, illumination, occlusion, head pose, and aging. In actual, human eyes can able to justify the authenticity of a person by the use of single image per class. In this paper, a new framework is proposed for nonlinear classification of face images with only one training image per class. Histogram Equalization...
In this paper, we present a new method to reconstruct a high-resolution (HR) face image from a low-resolution (LR) observation. Inspired by position-patch based face hallucination approach, we design position-based dictionaries to code image patches, and recovery HR patch using the coding coefficients as reconstruction weights. In order to capture nonlinear similarity of face features, we implicitly...
Both face detection and face recognition have started to be used widely these days in various applications such as biometric, surveillance, security, advertisement, entertainment, and so on. The ever increasing input image size in face detection and the large input DB in face recognition keep requiring more computational power to achieve real-time processing. Recently, embedded GPUs have started to...
We propose polynomial-time algorithms that sparsify planar and bounded-genus graphs while preserving optimal or near-optimal solutions to Steiner problems. Our main contribution is a polynomial-time algorithm that, given an unweighted graph G embedded on a surface of genus g and a designated face f bounded by a simple cycle of length k, uncovers a set F in E(G) of size polynomial in g and k that contains...
Though a variety of face recognition techniques have been proposed in the literature, only a few of them considered open set recognition problems, which involves the rejection of unregistered subjects in addition to identifying persons registered in the database. Transductive confidence machine (TCM) is a novel strategy for classification associated with valid confidence, with recognition reliability...
In the past few years, sparse representation classifier (SRC) has attracted great attention and widely used in human face recognition. Kernel sparse representation classifier (KSRC) based Metaface dictionary learning (MFL) is discussed in this paper. KSRC is a nonlinear extension of SRC. Through kernel trick, samples are mapped into an unknown kernel feature space first and then SRC will be used in...
A gender classification system uses human face from a given image to tell the gender of the given person. An effective gender classification approach is able to promote the improvement of many other applications, including image/video retrieval, security monitor, human-computer interaction, etc. In this paper, a method for gender classification task in frontal face images based on stacked-autoencoders...
Recently, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have achieved superior performance in pattern classification. Collaborative representation based classification with regularized least square (CRC_RLS) which uses l2 -norm is a very simple yet much more efficient scheme for face recognition (FR). Motivated by the fact that kernel...
Since mirror-like odd and even features in face recognition reflect the symmetrical and asymmetrical image information, respectively, their proper combination can improve the recognition rates to some extent. However, the face imaging process can easily be affected by external factors and encounter the noise signal, which disturbs the effect of face recognition based on combinational mirror-like odd...
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