Face Image Quality Quantitative Assessment for Forensic Identification of Human Images

Zeng, Jinhua; Zhu, Hongqing; Shu, Shi; Qiu, Xiulian

doi:10.1109/pic.2018.8706327

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…In (ZENG et al, 2018), a face image quality assessment method is proposed to improve face verification tasks in forensic science. The authors proposed a quantitative analysis based on the verification performance of the face verification system, as a measure of the face image quality.…”

Section: Fiqamentioning

confidence: 99%

Convolutional neural network for distortion Classification in face images.

Reina¹,

Patrícia²

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Face processing algorithms are becoming more popular in recent days due to the great domain of application in which they can be used. As a consequence, research about the quality of face images is also increasing. The current approach to Face Image Quality Assessment (FIQA) is focused on improving the performance of face recognition systems, as a result, current FIQA algorithms don't provide an indication of quality, but a performance estimation for face recognition algorithms. This approach makes the FIQA algorithms potentially unsuited for other scenarios regarding face images, and susceptible to inherit the limitations of face recognition. The present work tackles the main limitations of the current FIQA algorithms by proposing a new approach based on the distortions affecting the images. We developed two models based on Convolutional Neural Networks (CNN), to classify facial images according to the type and the degree of the distortion present in them. The models' output provides qualitative information about the quality of facial images, useful for face recognition systems, as well as other face processing algorithms. Additionally, the proposed method can be a starting point to image enhancement processes like denoising, and deblurring. Two other contributions can be outlined from this work: a comprehensive study about the impact of blur, noise, brightness, contrast, and JPEG compression in face processing algorithms; and a new dataset for image quality assessment and distortion classification in face images.

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Section: Fiqamentioning

confidence: 99%

Convolutional neural network for distortion Classification in face images.

Reina¹,

Patrícia²

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Efficient bi-traits identification using CEDRNN classifier for forensic applications

Johnson¹,

Chitra²,

Bamini³

2022

Expert Systems with Applications

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The Effects of AI-Driven Face Restoration on Forensic Face Recognition

Yang,

Li,

Zeng

2024

Applied Sciences

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In biometric recognition, face recognition is a mature and widely used technique that provides a fast, accurate, and reliable method for human identification. This paper aims to study the effects of face image restoration for forensic face recognition and then further analyzes the advantages and limitations of the four state-of-the-art face image restoration methods in the field of face recognition for forensic human image identification. In total, 100 face image materials from an open-source face image dataset are used for experiments. The Gaussian blur processing is applied to simulate the effect of blurred face images in actual cases of forensic human image identification. Four state-of-the-art AI-driven face restoration methods are used to restore the blurred face images. We use three mainstream face recognition systems to evaluate the recognition performance changes of the blurred face images and the restored face images. We find that although face image restoration can effectively remove facial noise and blurring effects, the restored images do not significantly improve the recognition performance of the face recognition systems. Face image restoration may change the original features in face images and introduce new made-up image features, thereby affecting the accuracy of face recognition. In current conditions, the improvement in face image restoration on the recognition performance of face recognition systems is limited, but it still has a positive role in the application of forensic human image identification.

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Face Image Quality Quantitative Assessment for Forensic Identification of Human Images

Cited by 3 publications

References 6 publications

Convolutional neural network for distortion Classification in face images.

Convolutional neural network for distortion Classification in face images.

Efficient bi-traits identification using CEDRNN classifier for forensic applications

The Effects of AI-Driven Face Restoration on Forensic Face Recognition

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