Fusion of transformed shallow features for facial expression recognition

Bougourzi, Fares; Mokrani, Karim; Ruichek, Yassine; Dornaika, Fadi; Ouafi, Abdelkrim; Taleb‐Ahmed, Abdelmalik

doi:10.1049/iet-ipr.2018.6235

Cited by 17 publications

(7 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By consulting a large number of relevant literature, we found that the research methods of dietary habits are single, and the nutritional status of the body caused by dietary habits includes many factors, such as energy, food type, macronutrients [ 22 ], micronutrients0 [ 23 ], and minerals [ 24 ], analyzing them separately cannot get the complete picture of athlete's nutritional status, and extracting key information can get the core of athlete's nutritional status and better serve sports practice. Therefore, we have made new attempts in methods, with the purpose of providing new ideas and methods for sports nutrition research.…”

Section: Research On the Diet Pattern Of Volleyball Playersmentioning

confidence: 99%

Relationship between Volleyball Sports Nutrition Food and Sports Athletes’ Training and Physical Health Based on Medical Image Recognition

Jian

2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

As we all know, the dietary nutrition of athletes has a great influence on physical condition and exercise ability. A good diet pattern is the basis of a reasonable diet for athletes. It helps to improve the function and physical state of athletes. This article is aimed at studying the impact of nutritious food on athletes’ training and physical health. This article proposes the relevant technology of medical image recognition, which is used to study the relationship between nutritious food and the health of volleyball players and athletes, and proposes methods such as weighing method, meal review method, and measurement method, and the purpose is to exercise nutritional research and provide new ideas and methods. In addition, 200 female volleyball players were randomly selected for comparative analysis. The experimental results in this paper show that the energy intake and energy consumption of the female intervention group maintained a balance after the intervention, and there was a significant change in the negative balance state before the intervention. The energy consumption changed from − 158.2 ± 156.2 to − 157.2 ± 129.6 . The number of athletes whose weight is close to the ideal range has increased from 44.8% to 48.5%.

show abstract

Section: Research On the Diet Pattern Of Volleyball Playersmentioning

confidence: 99%

Relationship between Volleyball Sports Nutrition Food and Sports Athletes’ Training and Physical Health Based on Medical Image Recognition

Jian

2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…In the last three decades, texture descriptors have proved their efficiency in many computer vision tasks. In our experiments, we used two of the most powerful descriptors: Local Phase Quantization (LPQ) [ 29 ] and Binarized Statistical Image Features (BSIF) [ 30 ]. In addition, we tested the combination of these two descriptors by concatenating their features alongside each other.…”

Section: Methodsmentioning

confidence: 99%

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

et al. 2021

Self Cite

View full text Add to dashboard Cite

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

show abstract

“…Turan and Lam performed 27 local descriptors while the Local Phase Quantization (LPQ) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) achieved the best results [ 25 ]. Bougourzi et al combined histogram of oriented gradients, local phase quantization, and binarized statistical image features to recognize the facial expressions from the static images [ 26 ]. Meena et al used graph signal processing along with the curvelet transform for recognizing the facial expressions.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition with LBP and ORB Features

Niu

Gao

Guo

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Emotion plays an important role in communication. For human–computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications used in real-life conditions, to gain better results without DNNs, in this paper, we propose an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and Local Binary Patterns (LBP) features extracted from facial expression. First of all, every image is passed through face detection algorithm to extract more effective features. Second, in order to increase computational speed, the ORB and LBP features are extracted from the face region; specifically, region division is innovatively employed in the traditional ORB to avoid the concentration of the features. The features are invariant to scale and grayscale as well as rotation changes. Finally, the combined features are classified by Support Vector Machine (SVM). The proposed method is evaluated on several challenging databases such as Cohn-Kanade database (CK+), Japanese Female Facial Expressions database (JAFFE), and MMI database; experimental results of seven emotion state (neutral, joy, sadness, surprise, anger, fear, and disgust) show that the proposed framework is effective and accurate.

show abstract

Fusion of transformed shallow features for facial expression recognition

Cited by 17 publications

References 57 publications

Relationship between Volleyball Sports Nutrition Food and Sports Athletes’ Training and Physical Health Based on Medical Image Recognition

Relationship between Volleyball Sports Nutrition Food and Sports Athletes’ Training and Physical Health Based on Medical Image Recognition

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Facial Expression Recognition with LBP and ORB Features

Contact Info

Product

Resources

About