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The paper addresses the problem of pose-invariant recognition of faces via an MRF matching model. Unlike previous costly matching approaches, the proposed algorithm employs effective techniques to reduce the MRF inference time. To this end, processing is done in a parallel fashion on a GPU employing a dual decomposition framework. The optimisation is further accelerated taking a multi-resolution approach...
While modern research in face recognition has focused on new feature representations, alternate learning methods for fusion of features, most have ignored the issue of unmodeled correlations in face data when combining diverse features such as similar visual regions, attributes, appearance frequency, etc. Conventional wisdom is that by using sufficient data and machine, one can learn the systematic...
The development of 3D face recognition algorithms that are robust to variations in expression has been a challenge for researchers over the past decade. One approach to this problem is to utilize the most stable parts on the face surface. The nasal region's relatively constant structure over various expressions makes it attractive for robust recognition and, in this paper, the use of features from...
The biometrics community enjoys an active research field that has produced algorithms for several modalities suitable for real-world applications. Despite these developments, there exist few open source implementations of complete algorithms that are maintained by the community or deployed outside a laboratory environment. In this paper we motivate the need for more community-driven open source software...
In this work we investigate a truly novel and extremely unique biometric problem: face-based recognition for transgender persons. A transgender person is someone who under goes a gender transformation via hormone replacement therapy; that is, a male becomes a female by suppressing natural testosterone production and exogenously increasing estrogen. Transgender hormone replacement therapy causes physical...
Face pair matching is the task of deciding whether or not two face images belong to the same person. This has been a very active and challenging topic recently due to the presence of various sources of variation in facial images, especially under unconstrained environment. We investigate cohort normalization that has been widely used in biomet-ric verification as means to improve the robustness of...
This article presents a new method aiming at automatically learning a visual similarity between two images from a class model. This kind of problem is present in many research domains such as object tracking, image classification, signing identification, etc. We propose a new method for facial recognition with a system based on non-linear projection and metric learning. To achieve this objective,...
The classical local binary pattern (LBP) method for facial feature description leads to a high feature dimensionality which requires expensive computational cost for face recognition and ignores the difference of contributions by different features in the same region. In this paper, we propose a structured sparse learning approach for efficient facial feature description. Firstly, a structured sparse...
This paper presents a parallel method for EBGM face recognition. Compared with other methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), EBGM has the advantage of higher accuracy, however, with more computational time and memory usage, which also mean less practicability. We propose a parallel method for EBGM by balancing the unit of images. We distribute the...
This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. We first develop a robust face tracking algorithm based on the local sparse appearance. This sparse representation model exploits both partial and spatial information of the face based on a covariance pooling method. Following in the face recognition stage, with the employment of a novel...
In order to retrieve impressive scenes from life log videos, we propose an emotional scene detection method based on facial expression recognition. Many researches about facial expression recognition focus on discriminating typical facial expressions such as happiness, sadness and surprise. But they are not suitable for life log videos because more complicated or subtle facial expressions are frequently...
Fisher criterion is one of most widely used methods for supervised feature selection. Traditional Fisher based feature selection methods focus on maximizing the distances inter-class and minimizing the distances of samples within the same class. But, they ignore the geometric structure of data in measuring the importance of the features. In this paper, we propose a new semi-supervised feature selection...
Face recognition is a topic of great interest in different areas, especially those related to security. The identification of a person by the image of her face is a difficult task because of changes experienced by the face due to various factors, such as facial expression, aging and even the lighting. This paper presents a new face recognition technique based on the combination of a competitive fuzzy...
Face is the key component in understanding emotions which play significant roles in many areas from security and entertainment to psychology and education. In this paper, we propose a method to detect facial action units in 3D face data by combining novel geometric properties and a new descriptor based on the Local Binary Pattern (LBP) methodology. The proposed method enables person and gender independent...
As a kind of statistical features, texture features often have a rotary deformation, and have strong resistibility to noise. The paper first constructs the gray level co-occurrence matrix of face image to describe texture feature of face image, and then uses the classification method of minimum weighted Euclidean distance to fulfill the matching and identification of face. Experiments results have...
This paper presents a hybrid technique for face recognition. The proposed technique consists of four stages: feature extraction, dimensionality reduction, feature selection, and classification. In the first stage, the features related to face images are obtained using Contourlet Transformation (CT). The dimension of features is reduced using Principal Component Analysis (PCA) to form more essential...
This work aims to propose an efficient hardware/software system fo guassian mixture model (GMM) parts-based topology modeling for face identification and verification. Following its great success in speaker recognition, The GMM approach was extended to face recognition providing a good trade-off in terms of complexity, performance and robustness. Despite its reduced complexity compared to other statistical...
It is very important and challenging to teach modern topics at the freshman level. This paper describes a biometrics module that fits into any introductory freshman engineering course. The project has broad learning outcomes, namely, enhanced application of math skills, software implementation skills, interest in biometrics and comprehension of ethical issues. Assessment results based on the analysis...
Face recognition for images acquired from uncontrollable environment and target positions is a challenging task. These input images are first pre-processed and initially aligned by the face detection algorithm. However, there are still some residual geometric errors after the initial alignment by the face detection algorithm. If we don't take these errors into account, the recognition performance...
To improve the discriminant nearest feature space analysis (DNFSA) methods [6], in this paper, we propose an improved DNFSA (IDNFSA) algorithm to increase the robustness for variable lighting face recognition. The IDNFSA removes the mean of each image and attempts to minimize the within-class feature space (FS) distance and maximize the between-class FS distance simultaneously. In the IDNFSA, the...
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