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Gender recognition from face images is a challenging problem with applications in various knowledge domains, such as biometrics, security and surveillance, human-computer interaction, among others. In this work, we propose and evaluate a novel method for gender recognition based on a geometric descriptor constructed from a pre-defined face shape model. The proposed approach, tested on four different...
Landmark detection has proven to be a very challenging task in biometrics. In this paper, we address the task of facial component-landmark detection. By “component” we refer to a rectangular subregion of the face, containing an anatomical component (e.g., “eye”). We present a fully-automated system for facial component-landmark detection based on multi-resolution isotropic analysis and adaptive bag-of-words...
In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human...
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 detection has advanced dramatically over the past three decades. Algorithms can now quite reliably detect faces in clutter in or near real time. However, much still needs to be done to provide an accurate and detailed description of external and internal features. This paper presents an approach to achieve this goal. Previous learning algorithms have had limited success on this task because the...
In this paper, we present an approach for detecting MTV video shot using Hidden Markov Models (HMMs), in which the color, shape and motion features are utilized. First, the temporal characteristics of different shot transitions are exploited and an HMM is constructed for shot transitions, including cut and gradual transitions. Secondly, a trained HMM are used to recognize the shot transition automatically,...
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