Bimodal gender recognition from face and fingerprint

Li, Xiong; Zhao, Xu; Fu, Yun; Liu, Yuncai

doi:10.1109/cvpr.2010.5539969

Cited by 24 publications

(18 citation statements)

References 21 publications

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“…There are other attempts to integrate generative and discriminative models. For example, [8,17,10] use generative models as priors over discriminative models and [24,12] learn generative models with the help of discriminative constraints. These methods are theoretically distinct from our method (as well as FESS and FK/TK methods).…”

Section: Discussionmentioning

confidence: 99%

Hybrid generative-discriminative classification using posterior divergence

Lee

Liu

2011

CVPR 2011

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Integrating generative models and discriminative models in a hybrid scheme has shown some success in recognition tasks. In such scheme, generative models are used to derive feature maps for outputting a set of fixed length features that are used by discriminative models to perform classification. In this paper, we present a method, called posterior divergence, to derive feature maps from the log likelihood function implied in the incremental expectationmaximization algorithm. These feature maps evaluate a sample in three complementary measures: (1) how much the sample affects the model; (2) how well the sample fits the model; (3) how uncertain the fit is. We prove that the linear classification error rate using the outputs of the derived feature maps is at least as low as that of plug-in estimation. We present efficient algorithms for computing these feature maps for semi-supervised learning and supervised learning. We evaluate the proposed method on three typical applications, i.e. scene recognition, face and non-face classification and protein sequence analysis, and demonstrate improvements over related methods.

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Section: Discussionmentioning

confidence: 99%

Hybrid generative-discriminative classification using posterior divergence

Lee

Liu

2011

CVPR 2011

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“…There have been some previous attempts to fuse multiple biometrics such as combining the face, gait, fingerprint, palm print and geometry for gender recognition [29][30][31]. Table 2 shows more related works for the fusion of physiological biometrics and the gap of this study.…”

Section: Gender Recognition Using Physiological Signalmentioning

confidence: 99%

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

et al. 2017

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Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani's work (90.34%), Nazarloo's work (92.50%), and other classifiers.

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“…Deceit detection, a less popular computation perception problem [2] also focuses on changes in cues in the face where specialized sets of features and datasets are dedicated to solving this problem. Age [3], [4] and gender [5] estimation similarly apply a myriad of techniques, but on a set of specialized features learned and tested from a task-dedicated dataset.…”

Section: Introductionmentioning

confidence: 99%

Shared features for multiple face-based biometrics

Nwogu

Zhou

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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People often make instant judgments about the age, health, mood, personality and character of others based on their facial features. It is not clear from a cognitive aspect whether these different traits require different sets of features or a shared feature set. Till date, much of the computational face image analysis work such as face recognition, face-based deceit detection, age estimation, gender estimation, etc, have been developed on datasets and features specific only to the problem-athand. In this paper, we explore an approach for performing face image analysis using a shared set of features for different tasks. By performing unsupervised learning on a large collection of face images, we learn the parameters of a probabilistic generative face model, and by projecting a new face image into this probabilistic space, we obtain a set of face features not created for any specific face analysis tasks. We investigate the use of such shared features and successfully predict the level of attractiveness, whether or not a face is made-up, the facial expression, and the gender of a person, given any arbitrary, near-frontal face image.

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Bimodal gender recognition from face and fingerprint

Cited by 24 publications

References 21 publications

Hybrid generative-discriminative classification using posterior divergence

Hybrid generative-discriminative classification using posterior divergence

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

Shared features for multiple face-based biometrics

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