Deep Stacked Autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor

نویسندگان

چکیده

Extracting an effective facial feature representation is the critical task for automatic expression recognition system. Local Binary Pattern (LBP) known to be a popular texture recognition. However, only few approaches utilize relationship between local neighborhood pixels itself. This paper presents Hybrid Texture Descriptor (HLTD) which derived from logical fusion of Neighborhood XNOR Patterns (LNXP) and LBP investigate potential positional pixel in emotion The LNXP encodes information based on two nearest vertical and/or horizontal neighboring current whereas center pixel. After fusion, Deep Stacked Autoencoder (DSA) established CK+, MMI KDEF-dyn dataset results show that proposed HLTD approach outperforms many state art methods with average rate 97.5% 94.1% 88.5% KDEF.

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ژورنال

عنوان ژورنال: Journal of Information Technology Research

سال: 2022

ISSN: ['1938-7857', '1938-7865']

DOI: https://doi.org/10.4018/jitr.2022010103