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The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we analyze two main approaches for expression recognition.
Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based...
This paper presents a facial expression recognition framework which infers the emotional states in real-time, thereby enabling the computers to interact more intelligently with people. The proposed method determines the face as well as the facial landmark points, extracts discriminating features from suitable facial regions, and classifies the expressions in real-time from live webcam feed. The speed...
In current years, the binate codify of facial features, being local binary patterns (LBP) and local ternary patterns (LTP) has grown into face recognition. Those confined facet descriptors subsidize a smooth and influential way for texture description. With this, we conclude an innovative process, LTP with Genetic Algorithm to extricate feature vector and segregated features through Support Vector...
Gender estimation has received increased attention due to its use in a number of pertinent security and commercial applications. Automated gender estimation algorithms are mainly based on extracting representative features from face images. In this work we study gender estimation based on information deduced jointly from face and body, extracted from single-shot images. The approach addresses challenging...
This paper proposes a speech/music classification system based on i-vector. An analysis of two classification methods, namely cosine distance score (CDS) and support vector machine (SVM) is performed. Two session compensation methods, within-class covariance normalization (WCCN) and linear discriminant analysis (LDA) are also discussed. The performance of proposed systems yields better results compared...
It is very difficult, if not impossible, to obtain a clean reference signal of a noisy speech recorded in a practical environment. As a result, intrusive methods that evaluate the quality of speech signal with the help of a clean reference signal has little value in real world applications. In this paper, we investigate the effectiveness of data-driven non-intrusive method for assessing quality of...
Now a days purchasing and selling products online has become more common. People often ask others about the product before purchasing, otherwise see the reviews about the product in the different e-commerce sites and then come to conclusion whether to buy the product or not. This decision making process is very important before purchasing any product. But it is not easy to read all the reviews because...
Vascular networks in infrared faces are created due to the blood flow under the skin. Variations in blood flow in the blood vessels cause temperature difference, which produces the vascular networks. This paper deals with binary classification of various infrared facial expressions using vascular network. The classification has been performed using Support Vector Machine classifier on five types of...
Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural...
In this paper, we present a real-time face recognition system for home security service robot which can be applied to recognize the person's face in front and give a warning when the identity of the person is a stranger. Considering the complexity of the actual situation, there might be some errors causing by the following factors like the angle, the size, the environment and the illumination of the...
Multi-spectral face recognition has been an active area of research over the past few decades. However, the vulnerability of multi-spectral face recognition systems is a growing concern that argues the need for Presentation Attack Detection (PAD) (or countermeasure or anti-spoofing) schemes to successfully detect targeted attacks. In this work, we present a novel feature descriptor LαMTiF that can...
Speech impaired people are detached from the mainstream society due to the lacking of proper communication aid. Sign language is the primary means of communication for them which normal people do not understand. In order to facilitate the conversation conversion of sign language to audio is very necessary. This paper aims at conversion of sign language to speech so that disabled people have their...
Texture analysis is an important research content in pattern recognition and computer vision, and we can get important information from the image through it. As an important method in feature extraction and classification, texture analysis has a very wide range of applications in the field of scientific research and engineering technology. In order to solve the problem of image classification, feature...
Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter,...
In this work, four well known convolutional neural networks (CNNs) that were pretrained on the ImageNet database are applied for the computer assisted diagnosis of celiac disease based on endoscopic images of the duodenum. The images are classified using three different transfer learning strategies and a experimental setup specifically adapted for the classification of endoscopic imagery. The CNNs...
In this era of globalisation and technology, determining the gender of a person from forenames has numerous applications especially in the machine translation and natural language processing fields. In this paper, we used a supervised machine learning approach to classify 10000 first names into either a male or female name. The names were manually extracted from an online telephone directory and then...
Automatic gender recognition, from face images, plays an important role in various biometric applications. This task has attracted the interest of not only computer vision researchers, but also of many psychologists. Inspired by the psychological results for human gender perception. There are two main purposes for this work. First; it aims at finding out which facial parts are most effective at making...
Hand gestures recognitions play an important role in human-computer interaction. To facilitate the understanding of computer vision-based hand gesture recognition, this paper describes a system for human-computer interaction through images' local features SURF, and we use threshold segmentation and bag-of-words algorithms to reduce the feature space dimensions. Leap motion is capable of collecting...
With the significant use of facial expressions in conveying information in human to human interaction, it has become quite imperative to be able to evaluate expressions especially for security reasons. Humans have grown to master ways of exhibiting misleading facial expressions and this has become part of our life style, maybe to avoid being rude or to hide malicious intent. This problem may be alleviated...
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