Improved gradient local ternary patterns for facial expression recognition

Holder, Ross Philip; Tapamo, Jules-Raymond

doi:10.1186/s13640-017-0190-5

Cited by 37 publications

(20 citation statements)

References 39 publications

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“…The method proposed by Holder and Tapamo for facial expression recognition, which involves improved gradient local ternary patterns (GLTPs), was extensively tested on the CK + and JAFFE datasets using a support vector machine. Their method was shown to further improve the accuracy and efficiency of the GLTPs obtained by other common and state-of-the-art methods [13].…”

Section: Literature Reviewmentioning

confidence: 98%

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Isnanto

Rochim

Eridani

et al. 2021

Int. j. eng. technol. innov.

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This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

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Section: Literature Reviewmentioning

confidence: 98%

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Isnanto

Rochim

Eridani

et al. 2021

Int. j. eng. technol. innov.

View full text Add to dashboard Cite

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“…SVM [41, 47, 48, 52, 53, 56, 58] is a well‐known supervised classification algorithm and also called a maximum margin classifier [61]. The hyperplane is selected with higher margin to discriminate two classes in n ‐dimensional space.…”

Section: Frequently Used Classifiers and Datasetsmentioning

confidence: 99%

Facial expression recognition techniques: a comprehensive survey

Rajan¹,

Chenniappan²,

Devaraj

et al. 2019

IET Image Processing

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Over the past decades, facial expression recognition (FER) has become an interesting research area and achieved substantial progress in computer vision. FER is to detect human emotional state related to biometric traits. Developing a machine based human FER system is a quite challenging task. Various FER systems are developed by analysing facial muscle motion and skin deformation based algorithms. In conventional FER system, the developed algorithms work on the constrained database. In the unconstrained environment, the efficacy of existing algorithms is limited due to certain issues during image acquisition. This study presents a detailed study on FER techniques, classifiers and datasets used for analysing the efficacy of the recognition techniques. Moreover, this survey will assist researchers in understanding the strategies and innovative methods that address the issues in a real-time application. Finally, the review presents the challenges encountered by FER system along with the future direction.

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“…Some attempts proposed extensions of LTP to solve the texture classification problem [30]- [35]. Some texture descriptors based LTP were proposed for face recognition, such as prominent LTP [36], relaxed LTP [37], adaptive LTP [38], co-occurrence of adjacent sparse LTP [39], improved gradient LTP [40]. Other research was based on LTP to develop texture descriptors for smoke detection, medical image analysis, fingerprint vitality, fall detection, and blur detection and segmentation [41]- [48].…”

Section: Related Workmentioning

confidence: 99%

Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties

Nga¹,

Pham-Viet²,

Nhat³

et al. 2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In this study, a proposed descriptor based on the improved local ternary patterns (ILTP) that also uses the color properties of rice varieties is presented. Not only gray-scale intensity, but R, G, and B color components of the rice grains are considered. Combining a support vector machine (SVM) with the proposed descriptor for classification of 17 rice varieties planted in Vietnam gives an overall accuracy of 95.53%. To evaluate and compare the effectiveness of the proposed descriptor with other analysis techniques for rice varieties classification, texture descriptors based on local binary pattern and local ternary patterns are combined with SVM to classify these 17 rice varieties. Experiment results show that the classification accuracy from the SVM using the proposed descriptor is significantly higher than using ILTP or other texture descriptors from the literature. This technique can be used to build an automatic system of rice varieties identification and classification.

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Improved gradient local ternary patterns for facial expression recognition

Cited by 37 publications

References 39 publications

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Facial expression recognition techniques: a comprehensive survey

Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties

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