Boosting gender recognition performance with a fuzzy inference system

作者：

Highlights：

• We used both inner and outer face cues.

• External cues improve classification performance for gender recognition.

• FIS framework improves classification results when combined with SVM.

• Unconstrained databases provide better results than that of constrained databases.

• We obtained 93.35% accuracy on Groups/LFW cross-database test.

摘要

•We used both inner and outer face cues.•External cues improve classification performance for gender recognition.•FIS framework improves classification results when combined with SVM.•Unconstrained databases provide better results than that of constrained databases.•We obtained 93.35% accuracy on Groups/LFW cross-database test.

论文关键词：Gender recognition,Fuzzy inference system,Fuzzy rules,Cross-database tests

论文评审过程：Available online 20 November 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.11.023