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In this paper, we propose the optimal parameters of local binary patterns such as type of pattern, size of blocks in the feature space and distance measure for face recognition using a genetic algorithm. The genetic algorithm is able to optimize all these parameters quickly and to improve the recognition accuracy. We provide a comparative study of three types of local binary patterns (LBP, LGP and...
The paper deals with evaluation of automatic training samples selection method based on self-organizing map (SOM) in face recognition systems. In earlier paper [1] we presented an approach for automatic training samples selection using various clustering algorithms with good results on the CMU PIE face database. We showed that with the use of SOM we can achieve a good training samples selection. In...
The problem of gender recognition using visual and acoustic cues has recently received significant attention. This paper explores the use of Total Variability (i-vectors) and Inter-Session Variability (ISV) modeling techniques for both unimodal and bimodal gender recognition, and compares them to several state-of-the-art algorithms. The experimental evaluation is conducted on the FERET and LFW databases...
This paper presents an algorithm to recognize 3D face under various conditions using 3D constrained local model (CLM-Z). We used a combination of 2D images (RGBs) and depth images (Ds) captured by Kinect which is an inexpensive and affordable sensor. Three-dimensional constrained local model was used for face-modeling and determining the face important points for robust face recognition under challenging...
The human visual cortex is extremely adept at distinguishing between male and female faces, or performing "Gender Classification". While the subject of face detection and recognition has received a lot of focus, research into the features or cognitive processes that are useful for identifying gender have received relatively little attention. Researchers have attempted to extract hand crafted...
Stochastic computing (SC) is a re-emerging technique to process probability data encoded in digital bit-streams. Its main advantage is that arithmetic operations can be implemented by extremely small and low-power logic circuits. This makes SC suitable for signal-processing applications involving matrix operations whose VLSI implementation is very costly. Previous SC approaches only address basic...
We performed an experimental study (n=48) of the effects of context congruency on human perceptions of robotic facial expressions across cultures (Western and East Asian individuals). We found that context congruency had a significant effect on human perceptions, and that this effect varied by the emotional valence of the context and facial expression. Moreover, these effects occurred regardless of...
According to the complex manifestation of human facial expression in realistic environment, occlusion problem has become a new challenge and a hot spot in the field of expression recognition. To make facial expression recognition applied in broader way, the main work is to increase the accuracy under different partial occlusion with feasible robust, which is limited by the information missing and...
Recognizing a person from a distance is important to establish meaningful social interaction and to provide additional cues regarding the situations experienced by a robot. To do so, face recognition and speaker identification are biometrics commonly used, with identification performance that are influenced by the distance between the person and the robot. This paper presents a system that combines...
In order to promote the utilization of lifelog videos, an effective retrieval framework of the emotional scenes, which are considered to be important scenes, is proposed in this paper. The proposed method is based on facial expression recognition since the emotional scenes can be detected by taking the facial expressions into consideration. Most of conventional facial expression recognition methods...
Face recognition is a research hotspot in recent years. In order to improve recognition accuracy of face recognition, a feature selection method for face image based on Gabor feature and recursive feature elimination was proposed in this paper. Firstly, Gabor features were extracted from face image. Then, face image was divided into pieces and Gabor feature statistics of these pieces were linked in...
Image-based kinship recognition is an important problem in the reconstruction and analysis of social networks. Prior studies on image-based kinship recognition have focused solely on pair wise kinship verification, i.e. on the question of whether or not two people are kin. Such approaches fail to exploit the fact that many real-world photographs contain several family members, for instance, the probability...
A new approach for video-based face recognition is presented in this paper. The proposed technique is based on the use of key points and related descriptors for image representation. In particular this work introduces a general approach to extend to the temporal dimension the analysis usually carried out on single images, with the aim of deriving a more stable and compact representation of video information...
Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training...
One of the biggest challenges faced by law enforcement entities in the present digital era, is fighting against online Child Sexual Abuse (CSA), due in particular to the massive amount of data that they receive for analysis. Pattern recognition system can provide an aid, e.g., to ease the identification of both the perpetrator and the victim of the crime. In particular, ancillary cues related the...
This paper presents cross-database evaluations of automatic appearance-based gender recognition methodology using normalized raw pixels and SVM classifier under unconstrained settings. Proposed method uses both histogram specification and feature space normalization on automatically aligned faces to achieve reliable recognition rate for real scenarios. Using a web based unconstrained training database,...
Roman coins play an important role to understand the Roman empire because they convey rich information about key historical events of the time. Moreover, as large amounts of coins are daily traded over the Internet, it becomes necessary to develop automatic coin recognition systems to prevent illegal trades. In this paper, we propose an automatic recognition method for ancient Roman coins. The proposed...
In this paper we present a new static descriptor for facial image analysis. We combine Gaussian derivatives with Local Binary Patterns to provide a robust and powerful descriptor especially suited to extracting texture from facial images. Gaussian features in the form of image derivatives form the input to the Linear Binary Pattern(LBP) operator instead of the original image. The proposed descriptor...
In this paper, we propose multiple facial action unit recognition by modeling their relations from both features and target labels. First, a multi-task feature learning method is adopted to divide action unit recognition tasks into several groups, and then learn the shared features for each group. Second, a Bayesian network is used to model the co-existent and mutual-exclusive semantic relations among...
This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an...
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