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Human re-identification is an important component in many application domains especially the automatic surveillance system. This paper proposes a robust method to re-identify persons using their face shapes based on the Active Shape Model (ASM) and the Procrustes Shape Analysis (PSA). The ASM-based technique is used to extract landmark points of each face image, as the feature. Then, the Procrustes...
Facial landmark detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial landmark detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work,...
We propose a Low-Dimensional Deep Feature based Face Alignment (LDFFA) method to address the problem of face alignment “in-the-wild”. Recently, Deep Bottleneck Features (DBF) has been proposed as an effective channel to represent input with compact, low-dimensional descriptors. The locations of fiducial landmarks of human faces could be effectively represented using low dimensional features due to...
The application of correlation filters for the task of facial landmark detection has been studied by many vision works. Their success, however, is limited by the presence of large pose variations, expression and occlusion in face images. Moreover, existing correlation filters may suffer from poor discrimination to distinguish visually similar landmarks such as the right and left eyes. In this work,...
This paper introduces and evaluates our point-triplet spin-image descriptor, a novel descriptor that requires three vertices to be computed. This descriptor is able to encode surface information, within a spherical neighbourhood with radius r defined from a triplet's baricenter, into a surface signature. We believe that this new descriptor could be useful within a number of graph based retrieval applications;...
We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded into the...
Traditional approaches to face recognition have utilized aligned facial images containing both shape and texture information. This paper analyzes the contributions of the individual facial shape and texture components to face recognition. These two components are evaluated independently and we investigate methods to combine the information gained from each of them to enhance face recognition performance...
In this paper, we investigate the(problem of 3D face authentication) use of AdaBoost-based learning algorithm which select the relèvent curves in the face. We use the shape of 3D curve for face authentication. The basic idea is the analysis of the shape of local facial set of cuves. The set of curves are extracted using level curves based representation centered on facial feature point landmarks...
In this paper, we propose a learning-based system for generating exaggerative caricatures with expression. Most of the previous works can only deal with frontal face images with neutral expression without glasses or hats, and are unable to apply more than one drawing prototype which was learned from the caricatures drawn by one single cartoonist at a time. The proposed caricature generation system...
Active Shape Model (ASM) is a powerful statistical tool for image interpretation, especially in face alignment. In the standard ASM, local appearances are described by intensity profiles, and the model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. It suffers from variations of poses, illumination and expressions. In this paper, an improved ASM framework,...
This paper summarizes results of face association experiments on real low resolution data from airport and the Labeled faces in the Wild (LFW) database. The objective of experiments is to evaluate different face alignment methods and their contribution to face association as such. The first alignment method used is Sequential Learnable Linear Predictor (SLLiP), originally developed for object tracking...
Face alignment is a critical problem in many face related applications such as facial expression analysis, face recognition, etc. This paper presents a novel real time face alignment algorithm based on active shape model (ASM). In our algorithm, local textures of each label point is used to predict the displacement of each label point by applying boost non-linear regressions on the local search stage...
A general framework of fusion at decision level, which works on ROCs instead of matching scores, is investigated. Under this framework, we further propose a hybrid fusion method, which combines the score-level and decision-level fusions, taking advantage of both fusion modes. The hybrid fusion adaptively tunes itself between the two levels of fusion, and improves the final performance over the original...
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