Thermal Face Recognition Under Temporal Variation Conditions

Hermosilla, Gabriel; Verdugo, José Luis; Castro, Gonzalo Farias; Pizarro, Francisco; Vera, Esteban

doi:10.1109/access.2017.2704296

Cited by 24 publications

(11 citation statements)

References 25 publications

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“…Table 5 , Table 6 and Table 7 compares the accuracy of our method to other algorithms in the literature using the databases with which they were published. Table 5 reports the algorithms evaluated by Hermosilla et al [ 53 ] using the UCHThermalFace database. Table 6 shows the accuracy achieved with the algorithms reported by Jo et al [ 26 ] with the CBSR NIR database.…”

Section: Resultsmentioning

confidence: 99%

“…Tested on different NIR data sets, NIRFaceNet achieves accuracies between 73.1% and 94.8%. Hermosilla et al [ 53 ] tested different methods of face recognition on two thermal IR databases, and they achieved their best accuracies using Gabor jet descriptors (96.6%), Weber local descriptors (94.9%), and LBP histograms (92.0%). To the best of our knowledge, none of the smart pixel circuits in the literature have been designed for or tested on IR images.…”

Section: Related Workmentioning

confidence: 99%

“…Accuracy of our RLBP+LDA and LBP+LDA methods compared to other face classification algorithms discussed in[53], using the UCH-Thermalface[78] database. Accuracy of our RLBP+LDA and LBP+LDA methods compared to other face classification algorithms discussed in[26], using the CBSR NIR[79] database.…”

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confidence: 99%

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Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital coprocessor that performs image classification. The SIS uses spatial gradients to compute a lightweight version of local binary patterns (LBP), which we term ringed LBP (RLBP). Our face recognition method, which is based on Ahonen’s algorithm, operates in three stages: (1) it extracts local image features using RLBP, (2) it computes a feature vector using RLBP histograms, (3) it projects the vector onto a subspace that maximizes class separation and classifies the image using a nearest neighbor criterion. We designed the smart pixel using the TSMC 0.35 μm mixed-signal CMOS process, and evaluated its performance using postlayout parasitic extraction. We also designed and implemented the digital coprocessor on a Xilinx XC7Z020 field-programmable gate array. The smart pixel achieves a fill factor of 34% on the 0.35 μm process and 76% on a 0.18 μm process with 32 μm × 32 μm pixels. The pixel array operates at up to 556 frames per second. The digital coprocessor achieves 96.5% classification accuracy on a database of infrared face images, can classify a 150×80-pixel image in 94 μs, and consumes 71 mW of power.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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“…Then, Thermal Minuta Points (TMP)-based feature vectors were employed for recognition. Vigneau et al [ 17 ] analyzed the problems resulting from temporal variations of infrared face images. They used five traditional feature-based methods to develop a thermal face recognition.…”

Section: Introductionmentioning

confidence: 99%

Thermal face recognition under different conditions

Lin

Chen

2021

BMC Bioinformatics

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Background A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a machine learning classifier is utilized for thermal face recognition. The steps of preprocessing, feature extraction and classification are incorporated in training phase. First of all, by using Bayesian framework, the human face can be extracted from thermal face image. Several thermal points are selected as a feature vector. These points are utilized to train Random Forest (RF). Random Forest is a supervised learning algorithm. It is an ensemble of decision trees. Namely, RF merges multiple decision trees together to obtain a more accurate classification. Feature vectors from the testing image are fed into the classifier for face recognition. Results Experiments were conducted under different conditions, including normal, adding noise, wearing glasses, face mask, and glasses with mask. To compare the performance with the convolutional neural network-based technique, experimental results of the proposed method demonstrate its robustness against different challenges. Conclusions Comparisons with other techniques demonstrate that the proposed method is robust under less feature points, which is around one twenty-eighth to one sixtieth of those by other classic methods.

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“…Infrared facial images also change over time. G. Hermosilla Vigneau et al [5] Observed the relationship between infrared image and time axis when used in facial recognition system. The results show that when infrared images of the face are acquired over time, the change results are obvious.…”

Section: Introductionmentioning

confidence: 99%

A Novel Facial Thermal Feature Extraction Method for Non-Contact Healthcare System

et al. 2020

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The non-contact healthcare system is a system that can avoid germ infection, and can also provide comfortable and convenient health care services for the caregivers. In the current thermal imaging research, local or small area images are used to represent the overall temperature information of the participants, but the temperature feature of the face should not only be used in a small part and ignore other parts. The facial thermal image can show clear temperature feature but is not conducive to meaningful feature extraction. Therefore, this research proposed a novel facial thermal image feature extraction method, which is used facial landmarks to detect and cut 12 blocks to establish a new feature matrix based on color mean values and standard deviation values. It can establish clear features on facial thermal images. The core part of the proposed healthcare system is the use of a deep learning framework, which is based on CAFFE under the DIGITS platform. The CAFFE runs the classic CNN, GoogLeNet. Based on the acquired images and new feature types, four models were trained, which were used for the raw RGB image, raw thermal image, RGB feature image, and thermal feature image. In the experiment, 800 images were used for training and validation, and 200 images were used for testing. An additional 40 images were used for random testing. The experimental results show that RGB images cannot be effectively used, thermal images can effectively predict the health status, and thermal feature images have the highest prediction accuracy. INDEX TERMS Non-contact healthcare system, thermal feature extraction, deep learning, GoogLeNet.

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Thermal Face Recognition Under Temporal Variation Conditions

Cited by 24 publications

References 25 publications

Face Recognition on a Smart Image Sensor Using Local Gradients

Face Recognition on a Smart Image Sensor Using Local Gradients

Thermal face recognition under different conditions

A Novel Facial Thermal Feature Extraction Method for Non-Contact Healthcare System

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