Local Entropy and Standard Deviation for Facial Expressions Recognition in Thermal Imaging

Elbarawy, Yomna M.; El-Sayed, Rania Salah; Ghali, Neveen I.

doi:10.11591/eei.v7i4.1230

Cited by 6 publications

(2 citation statements)

References 16 publications

(14 reference statements)

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“…thermal imaging records the heat radiation from the face and body temperature even in a completely dark area, it has been suggested as an answer in the latest years [2][3][4]. The likelihood of creating a fake face pattern is greatly diminished since the temperature of the skin on the face is directly tied to the underlying blood vessels that are specific to every unique person [5].…”

Section: International Journal Of Electrical and Computer Engineering...mentioning

confidence: 99%

Human Face Emotions Recognition from Thermal Images Using DenseNet

Prasad

Chandana

2023

Int. j. electr. comput. eng. syst. (Online)

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In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

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Section: International Journal Of Electrical and Computer Engineering...mentioning

confidence: 99%

Human Face Emotions Recognition from Thermal Images Using DenseNet

Prasad

Chandana

2023

Int. j. electr. comput. eng. syst. (Online)

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“…An approach has been proposed by Elbarawy et al [ 23 ] to solve the expression recognition problem in thermal images using feature extractors and a classifier. The feature extractors used in their approach were the discrete cosine transform (DCT) and local energy (LE) filters, and the classifier used was the KNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient thermal face recognition method using optimized curvelet features for biometric authentication

Ali

Eltoukhy

et al. 2023

PLoS ONE

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Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user’s face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

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