Multimodality gender estimation using Bayesian hierarchical model

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

doi:10.1109/icassp.2010.5495242

Cited by 2 publications

(1 citation statement)

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“…It is observed that limited work has been is carried out for identifying a person's gender using their multi-modal biometrics. This section reviews the studies which have been reported on gender identification using multi-modal biometric, Xiong Li et al [39] have performed multimodal based gender identification by combining local binary patterns and Bag of words features based on decision level fusion on the face and fingerprint traits of the internal database of 397 volunteers from Han nationality and obtained an accuracy of 94% using Bayesian Hierarchical model. Mohamed A et al [40] have performed multimodal based gender identification by combining features binary features, Eigenvalue, Syntactic Complexity, Response length, shallow and deep syntax and mean heart rate max-min difference features are combined on five different traits i.e.…”

Section: Relatesd Workmentioning

confidence: 99%

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

Patil¹,

Kruthi²,

Gornale³

2019

IJIGSP

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A certain number of researchers have utilized uni-modal bio-metric traits for gender classification. It has many limitations which can be mitigated with inclusion of multiple sources of biometric information to identify or classify user's information. Intuitively multimodal systems are more reliable and viable solution as multiple independent characteristics of modalities are fused together. The objective of this work is inferring the gender by combining different biometric traits like face, iris, and fingerprints of same subject. In the proposed work, feature level fusion is considered to obtain robustness in gender determination; and an accuracy of 99.8% was achieved on homologous multimodal biometric database SDUMLA-HMT (Group of Machine Learning and Applications, Shandong University). The results demonstrate that the feature level fusion of Multimodal Biometric system greatly improves the performance of gender classification and our approach outperforms the state-of-the-art techniques noticed in the literature.

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Section: Relatesd Workmentioning

confidence: 99%