Face recognition in a large dataset using a hierarchical classifier

Abbaspoor, Navid; Hassanpour, Hamid

doi:10.1007/s11042-022-12382-5

Cited by 3 publications

(2 citation statements)

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“…The proposed method involves using a neural network with multiple layers to analyze facial images and learn expression characteristics. The approach was tested on the FERET database, which did not include masks, and the results show that it achieves better recognition accuracy than other methods [14]. Furthermore, deep learning has succeeded in single-domain datasets, and current research combines multimodal inputs.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal CNN Features Fusion for Emotion Recognition: A Modified Xception Model

Shahzad,

Bhatti,

Jaffar

et al. 2023

IEEE Access

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Facial expression recognition (FER) is advancing human-computer interaction, especially, today, where facial masks are commonly worn due to the COVID-19 pandemic. Traditional unimodal techniques for facial expression recognition may be ineffective under these circumstances. To address this challenge, multimodal approaches that incorporate data from various modalities, such as voice expressions, have emerged as a promising solution. This paper proposed a novel multimodal methodology based on deep learning to recognize facial expressions under masked conditions effectively. The approach utilized two standard datasets, M-LFW-F and CREMA-D, to capture facial and vocal emotional expressions. A multimodal neural network was then trained using fusion techniques, outperforming conventional unimodal methods in facial expression recognition. Experimental evaluations demonstrated that the proposed approach achieved an accuracy of 79.81%, a significant improvement over the 68.81% accuracy attained by the unimodal technique. These results highlight the superior performance of the proposed approach in facial expression recognition under masked conditions.INDEX TERMS Deep learning, multimodal fusion techniques, neural network, facial expression under the mask.

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Section: Introductionmentioning

confidence: 99%

Multi-Modal CNN Features Fusion for Emotion Recognition: A Modified Xception Model

Shahzad,

Bhatti,

Jaffar

et al. 2023

IEEE Access

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“…Especially in a large database, the face recognition model errs in distinguishing features. Previous works have used various methods like clustering [26] and hashing [27] to increase face recognition accuracy in uncontrolled conditions. These methods work well on a large dataset problem and the angularity of faces.…”

Section: Introductionmentioning

confidence: 99%

Empowering Face Recognition Methods using a GAN-based Single Image Super-Resolution Network

Shahbakhsh¹,

Hassanpour²

2022

IJE

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Face recognition is one of the most common authentication techniques widely used due to its easy access. In many face recognition applications, captured images are often of low resolution. Face recognition methods perform poorly on low resolution images because they are trained on high resolution face images. Although existing face hallucination methods may generate visually pleasing images, they cannot improve the performance of face recognition methods at low resolution as the structure of the face image and high-frequency details are not sufficiently preserved. Recent advances in deep learning have been used in this paper to propose a new face super-resolution approach to empower face recognition methods. In this paper, a Generative Adversarial Network is used to empower face recognition in low-resolution images. This network considers image edges and reconstructs high-frequency details to preserve the face structure. The proposed technique to generate super-resolved features is usable in any face recognition method. We have used some state-of-the-art face recognition methods to evaluate the proposed method. The results showed a significant impact of the proposed method on the accuracy of face recognition of low resolution images.

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