Range Image Derivatives for GRCM on 2.5D Face Recognition

Chong, Lee Ying; Teoh, Andrew Beng Jin; Ong, Thian Song

doi:10.1007/978-981-10-0557-2_73

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Classification rate Alyuz et al [15] 97.5% Elaiwat et al [16] 94.4% Drira et al [17] 97.0% Faltemier et al [18] 97.2% Chong et al [19] 90.87% Our method 98.9% [15] 95.9% Li et al [20] 95.4% Our method 97.3%…”

Section: Methodsmentioning

confidence: 98%

Deep Residual Feature Quantization for 3D Face Recognition

Hariri

Zaabi²

2021

Preprint

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3D face recognition (FR) has been successfully applied using Convolutional neural networks (CNN) which have demonstrated stunning results in diverse computer vision and image classification tasks. Learning CNNs, however, need to estimate millions of parameters that expect high-performance computing capacity and storage. To deal with this issue, we propose an efficient method based on the quantization of residual features extracted from ResNet-50 pre-trained model. The method starts by describing each 3D face using a convolutional feature extraction block, and then apply the Bag-of-Features (BoF) paradigm to learn deep neural networks (we call it Deep BoF). To do so, we apply Radial Basis Function (RBF) neurons to quantize the deep features extracted from the last convolutional layers. An SVM classifier is then applied to classify faces according to their quantized term vectors. The obtained model is lightweight compared to classical CNN and it allows classifying arbitrary-sized images. The experimental results on the FRGCv2 and Bosphorus datasets show the powerful of our method compared to state of the art methods.

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