1,197 research outputs found
Fast Gender Recognition by Using a Shared-Integral-Image Approach
[[abstract]]We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear support vector machines for classification, and obtain more accurate identification results.[[conferencetype]]國際[[conferencedate]]20090419~20090424[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa
Smile detection in the wild based on transfer learning
Smile detection from unconstrained facial images is a specialized and
challenging problem. As one of the most informative expressions, smiles convey
basic underlying emotions, such as happiness and satisfaction, which lead to
multiple applications, e.g., human behavior analysis and interactive
controlling. Compared to the size of databases for face recognition, far less
labeled data is available for training smile detection systems. To leverage the
large amount of labeled data from face recognition datasets and to alleviate
overfitting on smile detection, an efficient transfer learning-based smile
detection approach is proposed in this paper. Unlike previous works which use
either hand-engineered features or train deep convolutional networks from
scratch, a well-trained deep face recognition model is explored and fine-tuned
for smile detection in the wild. Three different models are built as a result
of fine-tuning the face recognition model with different inputs, including
aligned, unaligned and grayscale images generated from the GENKI-4K dataset.
Experiments show that the proposed approach achieves improved state-of-the-art
performance. Robustness of the model to noise and blur artifacts is also
evaluated in this paper
Machine Analysis of Facial Expressions
No abstract
Understanding critical factors in gender recognition
Gender classification is a task of paramount importance in face recognition research, and it is potentially useful in a large set of applications. In this paper we investigate the gender classification problem by an extended empirical analysis on the Face Recognition Grand Challenge version 2.0 dataset (FRGC2.0). We propose challenging experimental protocols over the dimensions of FRGC2.0 – i.e., subject, face expression, race, controlled or uncontrolled environment. We evaluate our protocols with respect to several classification algorithms, and processing different types of features, like Gabor and LBP. Our results show that
gender classification is independent from factors like the race of the subject, face expressions, and variations of controlled illumination conditions. We also report that Gabor features seem to be more robust than LBPs in the case of uncontrolled environment
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