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Sparse representation based classification (SRC) has been introduced as a new algorithm for face recognition classification instead of the classical gradient-based algorithms. However, there are some limitations that influence the robustness properties in SRC. One of the most effective parameters that impacts the SRC performance is the directory of training samples. It should contain enough samples...
The application of convolution neural network provides numerous opportunities to get performance of face recognition boosted. These opportunities require efficient deep face network structures as well as optimization methods for large datasets. However, existing deep face network structures mainly focus on stacking more convolutional layers while ignoring the importance of width. In this work, we...
In this paper we propose a new post-processing approach for dimensionality reduction methods based on multidimensional ensemble empirical mode decomposition (MEEMD). In the proposed method, the features are decomposed into different components and then we maximize the dependency and the dispersion between classes thanks to Gaussian filter and Butterworth filter. The performance of the proposed algorithm...
Patch-based face hallucination algorithms utilize either local patches (e.g., position-patch approaches) or nonlocal patches (e.g., dictionary-learning approaches) to exploit self-similarity prior from training samples. Although they yield decent results, solo source patches limit their performance due to not fully taking self-similarity prior from both local and nonlocal ones. In order to overcome...
As a new biometric, the Electroencephalogram (EEG) signal has the advantages of invisibility, non-clonability, and non-coercion compare to traditional biometrics. However, the real-time and stability are the difficulties that the current EEG-based person authentication systems face. In this paper, we design a real-time and stable person authentication system using EEG signals, which are elicited by...
Some challenges of developing face recognition system is to train examples of different poses, illuminations, contrast and background conditions. Even though if the system works okay with the tested data set; it fails big time to perform accurately with new data set with different attributes. This manuscript is focusing on the mentioned problem statement by deploying PCA and implementing the concept...
Facial expression recognition is a hot research direction of pattern recognition and computer vision. It has been increasingly used in artificial intelligence, human-computer interaction and security monitoring in recent years. Convolution neural network (CNN) as a depth learning architecture can extract the essential features of the image, and in the case of large changes in shooting conditions,...
Face recognition is an active and challenging task in pattern recognition and computer vision application. Sparse representation based classification has been verified to be powerful for face recognition. This paper proposes the metaface block sparse bayesian learning (MBSBL) based on the framework of sparse representation. The MBS-BL combines the metaface learning and block sparse bayesian learning...
Since the Viola-Jones seminal work, the boosted cascade with simple features has become the most popular and effective approach for practical face detection. More improved face detectors that can handle uncontrolled face detection scenarios have achieved by applying more advanced features such as Histogram of oriented Gradients (HoG). The great improvement in accuracy delivered by these methods has...
Face gender classification plays an important role in pattern recognition, and it is a challenging research direction, but the current research is not perfect. We use the MB-LBP operator to extract the facial texture feature, which can be used as the training sample of the gender classifier, and it reduces the influence of the noise in the complex facial image. In addition, we propose the method of...
Recognizing human faces is one of the most popular problems in the field of pattern recognition. Many approaches and methods have been tested and applied on the topic, especially neural networks. This paper proposed a new loss layer that can be replaced at the bottom of a neural network architecture in terms of face recognition, called constrained triplet loss layer (CTLL). In order to make more confident...
Face verification has been widely studied due to its importance in surveillance and forensics applications. In this paper, our goal is to verify identity of a pairwies sample, where each sample contains two heterogeneous facial images captured under different scenarios (e.g., low-resolution vs. high-resolution, or occluded facial image vs. non-occluded one). In oder to address the appearance difference...
Growing number of surveillance and biometric applications seek to recognize the face of individuals appearing in the viewpoint of video cameras. Systems for video-based FR can be subjected to challenging operational environments, where the appearance of faces captured with video cameras varies significantly due to changes in pose, illumination, scale, blur, expression, occlusion, etc. In particular,...
Collaborative representation classification (CRC) has attracted increasing attention in face recognition (FR) tasks. The two-phase sparse representation (TPSR) methods are the improved schemes. However, most TPSR methods decrease training samples in the first step, resulting in less similarities or discrimination for representation, even unstable classification. In this paper, we propose a new two-phase...
The output encodings of neural nets determine the structure of the space in which inference occurs. Yet, they are generally given very little thought. It is common practice for neural nets to use 1-Hot encoding when training to discriminate among many classes. The primary exceptions to this are error correcting output codes, and semantic output encodings. Output encodings based upon semantic descriptors...
The face detection algorithm based on multi-channel map discriminant projection Haar feature is proposed in this paper. The multi-channel map is extracted from the face image, which can reduce the influence of the illumination and the noise in the image. Based on the positive and negative training samples, the enhanced Haar feature are obtained by the linear discriminant projection, which can improve...
A practical method to improve the performance of off-line Handwritten Chinese Character Recognition (HCCR) was proposed in this paper. The center loss was used in face verification task to optimize intra-class distance. With the joint supervision of softmax loss and center loss, A light convolutional neural networks (CNNs) framework was trained for off-line HCCR which could optimize inter and intra...
Eye state recognition is still challenging in the field of computer vision. Many researchers have reported that their methods can work well with frontal face views, but not with variations of head poses. Some have described that their methods deal effectively with head pose problems, but the systems are complex to implement and consume a lot of processing time. In this paper, a novel method of eye...
The Moroccan Higher Education System has been based until now on actual physical students' attendance to courses and lectures as one of its compulsory features. In this way, students, who, for some reason or another, cannot attend, find themselves at a loss and with no available follow-up means to access, the course or lecture contents delivered in class. At present, given the availability of distance...
Planar spoofing is a well researched problem, wherein a high quality planar photograph can be replayed in front of a still camera as a substitute for another individual's face. Most modern day face recognition systems can be fooled by this process, as the perceptual information contained in a photo-of-a-photo, is virtually the same as that of a natural photograph of an individual. Current solutions...
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