Gradient-Based Compact Binary Coding for Facial Expression Recognition

doi:10.22266/ijies2021.1231.34

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Still, it does not merge various techniques for enhancing the accuracy associated with the system. This work presents a unique face descriptor, Gradient Direction Pattern (GDP), for FER [12]. With the use of gradients, GDP encoded the format of the face picture in a more compact fashion so that the FER was resilient to position fluctuations.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Facial Expression Recognition Using Novel Enhanced Gated Recurrent Unit-based Kookaburra Optimization Algorithm

2024

IJIES

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Research on real-time Facial Expression Recognition (FER) and human emotion prediction has been ongoing. The desire of humans to enable cohabitation between humans as well as machines can only be realized if a machine is able to comprehend, display, and respond to a human's emotional state. If an Artificial Intelligence (AI) makes judgements on the basis of emotions rather than just algorithms and logic, it will be able to really breathe life without air. Although there have been several attempts to date to do this, it is still believed to be in the early stages of development. Hence, this paper plans to perform the FER prediction with the help of novel intelligent deep learning technology. Initially, the data is gathered from two standard benchmark sources such as FER 2013 and EMOTIC dataset. Next, the pre-processing of the gathered images is accomplished by face detection, rotation rectification, and Local Binary Pattern (LBP). From the pre-processed images, the feature extraction is performed by the Centralized Binary Pattern (CBP) method. These extracted features undergo the final prediction phase that is done by the novel Enhanced Gated Recurrent Unit (EGRU), in which the parameters of GRU are tuned by the nature inspired optimization algorithm referred as Kookaburra Optimization Algorithm (KOA) with the consideration of error minimization as the major objective function. This novel EGRU-KOA predicts the final outcome with consideration of various facial expressions such as sad, surprise, disgust, contempt, happy, and fear respectively. The novelty of the proposed model is demonstrated by comparing it with various conventional models in terms of distinct analysis respectively. The proposed EGRU-KOA in terms of accuracy is 2.78%, 5.60%, 1.63%, and 4.28% better than DBN+QPSO, GDP, CNN, and WKELM respectively. Similarly, the proposed EGRU-KOA with respect to MSE is 84.82%, 87.41%, 88.51%, and 86.82% better than DBN+QPSO, GDP, CNN, and WKELM respectively.

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Section: Related Workmentioning

confidence: 99%

Predicting the Facial Expression Recognition Using Novel Enhanced Gated Recurrent Unit-based Kookaburra Optimization Algorithm

2024

IJIES

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Directional Edge Coding For Facial Expression Recognition System

Subbareddy,

Sandhya,

Pavani

2023

2023 IEEE 7th Conference on Information and Communication Technology (CICT)

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WELDP: Weighed Edge Local Directional Pattern for expression recognition from facial images

Ramyasree,

Kumar

2023

IFS

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At present, the local appearance-based texture descriptors in Facial Expression Recognition have limited accuracy due to the inability to encode the discriminative edges. The major cause is the presence of distorted and weak edges due to noise. Hence, this paper proposes new Expression Descriptor called as Weighted Edge Local Directional Pattern (WELDP) which can discriminate the weak and strong edges. Unlike the conventional local descriptors, WELDP searches for the support of neighbor pixels in the determination of Facial expression attributes such as Edges, Corners, Lines, and Curved Edges. WELDP encodes only Strong edge responses and discards weaker edge responses after extracting them through edge detection masks. This work adapted two masks for edge detection: they are Robinson Compass Mask and Kirsch Compass Mask. Moreover, the WELDP aims at code redundancy and encode each pixel only with seven bits (one sign bit and six directional bits). Then the WELDP image is described by a histogram and then processed through SVM (Support Vector Machine) for expression identification. From the simulation experiments, the proposed WELDP is found as better than several existing methods.

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Gradient-Based Compact Binary Coding for Facial Expression Recognition

Cited by 3 publications

References 31 publications

Predicting the Facial Expression Recognition Using Novel Enhanced Gated Recurrent Unit-based Kookaburra Optimization Algorithm

Predicting the Facial Expression Recognition Using Novel Enhanced Gated Recurrent Unit-based Kookaburra Optimization Algorithm

Directional Edge Coding For Facial Expression Recognition System

WELDP: Weighed Edge Local Directional Pattern for expression recognition from facial images

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