Open AccessCNN BLSTM Joint Technique on Dynamic Shape and Appearance of FACS
Author(s) -
Nazmin Begum,
A Syed Mustafa
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d7308.049420
Subject(s) - computer science , convolutional neural network , expression (computer science) , joint (building) , artificial intelligence , face (sociological concept) , facial expression , process (computing) , deep learning , pattern recognition (psychology) , speech recognition , computer vision , architectural engineering , social science , sociology , engineering , programming language , operating system
Facial recognition is a process where we can identify or verify a person from digital image or videos and is used in ID verification services , protecting law enforcement ,preventing retail crime etc. Past work on automatic analysis of facial expression focuses on detecting the facial expression and exploiting the dependencies among AU’s. But, spontaneous detection of facial expression depending on various factors such as shape, appearance and dynamics is very difficult. Joint learning of shape , appearance and dynamics is done by a deep learning technique.This includes a convolutional neural networks and bidirectional long short term memory(CNN-BLSTM). This combination of CNN-BLSTM excels the modeling of temporal information. FERA2015 dataset achieves the state of art.