Fine-tuning Approach to NIR Face Recognition

Kim, Jeyeon; Jo, Hoon; Ra, Moonsoo; Kim, Whoi-Yul

doi:10.1109/icassp.2019.8683261

Cited by 7 publications

(30 citation statements)

References 14 publications

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“…In this scenario, the validation rate is decreased because the gallery and probe images have large intensity differences around the eye regions due to reflected light. The validation rates of the Kim's method [9] are less than 93% in the scenario, as shown in Fig. 1 (b).…”

Section: Introductionmentioning

confidence: 96%

“…However, Kim et al [9] showed that the validation rate of RGB FR decreases significantly under poor lighting conditions. In these environments, the validation rate of Kim's near infrared (NIR) FR method [9] was 40% or more higher than that of RGB FR. Since such environments are common in FR scenarios, such as unlocking a cell phone with FR in a dark room, it is important to research the field of NIR FR.…”

Section: Introductionmentioning

confidence: 99%

“…Most deep convolutional neural networks (DCNN)-based face recognition (FR) studies have been conducted using RGB face images [1]- [8]. However, Kim et al [9] showed that the validation rate of RGB FR decreases significantly under poor lighting conditions. In these environments, the validation rate of Kim's near infrared (NIR) FR method [9] was 40% or more higher than that of RGB FR.…”

Section: Introductionmentioning

confidence: 99%

“…Since such environments are common in FR scenarios, such as unlocking a cell phone with FR in a dark room, it is important to research the field of NIR FR. Even though the Kim's method [9] has significantly improved the accuracy by introducing the finetuning approach into NIR FR, DCNN-based NIR FR still has…”

Section: Introductionmentioning

confidence: 99%

“…One of the main issues with the existing NIR FR studies [9]- [11] is that their performances with respect to accuracy and validation rates are significantly reduced in Glasses and Non-glasses (G-NG) positive NIR FR scenarios. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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A DCNN-Based Fast NIR Face Recognition System Robust to Reflected Light From Eyeglasses

Kim

Ra²,

Kim

2020

IEEE Access

Self Cite

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Due to an increasing need for face recognition under poor lighting conditions, near infrared (NIR) face recognition based on deep convolutional neural networks (DCNN) has become an active area of research. However, in NIR face images of eyeglasses wearers, reflected light is generated around the eyes due to active NIR light sources, and it is one of the main contributors to performance degradation in NIR face recognition. In addition, there have to date been no attempts to lighten DCNN models for NIR face recognition. To solve these problems, we propose a DCNN-based fast NIR face recognition system which is robust to reflected light. This work has two main contributions: 1) We generated synthetic face images of individuals with and without eyeglasses using our proposed CycleGAN-based Glasses2Non-glasses (G2NG) data augmentation. We then constructed an augmented training database by adding the synthetic images, and the database helps to make the NIR face recognition system robust against reflected light. 2) A lightweight NIR FaceNet (LiNFNet) architecture was developed to reduce the computational complexity of the proposed system by adapting the depthwise separable convolutions and linear bottlenecks to VGGNet 16. The proposed architecture reduces the computation required, while improving the performance of NIR face recognition. Through the experiments reported in this paper, we verified that the proposed G2NG data augmentation improved the face recognition validation rate by 99.09% for NIR face images which have the reflected light from eyeglasses. Also, LiNFNet reduces the number of multiplication operations by 4.4 × 10 9 compared with VGGNet 16. INDEX TERMS Biometrics, deep learning, NIR face identification, fine-tuning, lightweight deep CNN.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A DCNN-Based Fast NIR Face Recognition System Robust to Reflected Light From Eyeglasses

Kim

Ra²,

Kim

2020

IEEE Access

Self Cite

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Recognition in the near infrared spectrum for face, gender and facial expressions

Salim

Srinath²,

Jayaraman

et al. 2021

Multimed Tools Appl

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Face recognition under mask-wearing based on residual inception networks

Moungsouy

Tawanbunjerd

Liamsomboon

et al. 2022

ACI

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PurposeThis paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.Design/methodology/approachThe proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.FindingsThe proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.Originality/valueThe convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

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Fine-tuning Approach to NIR Face Recognition

Cited by 7 publications

References 14 publications

A DCNN-Based Fast NIR Face Recognition System Robust to Reflected Light From Eyeglasses

A DCNN-Based Fast NIR Face Recognition System Robust to Reflected Light From Eyeglasses

Recognition in the near infrared spectrum for face, gender and facial expressions

Face recognition under mask-wearing based on residual inception networks

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