Can we boost the power of the Viola–Jones face detector using preprocessing? An empirical study

Afifi, Mahmoud; Nasser, Marwa; Korashy, Mostafa; Rohde, Katherine; Mohamed, Aly A.

doi:10.1117/1.jei.27.4.043020

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…An incorrect classification may occur if a person wears glasses (Li et al, 2018) or wears makeup (Kuehlkamp et al, 2017). Other problems occur when facial images are obtained under low light conditions (Afifi and Abdelhamed, 2017;Afifi et al, 2018).…”

Section: Gender Recognition Using Visible Imagesmentioning

confidence: 99%

Gender Recognition Based on Hand Thermal Characteristic

Prihodova¹

2022

AIP

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Automatic gender recognition is one of the frequently solved tasks in computer vision. It is useful for analysing human behaviour, intelligent monitoring or security. In this article, gender is recognized based on multispectral images of the hand. Hand (palm and back) images are obtained in the visible spectrum and thermal spectrum; then a fusion of images is performed. Some studies say that it is possible to distinguish male and female hands by some geometric features of the hand. The aim of this article is to determine whether it is possible to recognize gender by the thermal characteristics of the hand and, at the same time, to find the best architecture for this recognition. The article compares several algorithms that can be used to solve this issue. The convolutional neural network (CNN) AlexNet is used for feature extraction. The support vector machine, linear discriminant, naive Bayes classifier and neural networks were used for subsequent classification. Only CNNs were used for both extraction and subsequent classification. All of these methods lead to high accuracy of gender recognition. However, the most accurate are the convolutional neural networks VGG-16 and VGG-19. The accuracy of gender recognition (test data) is 94.9% for the palm and 89.9% for the back. Experiments in comparative studies have had promising results and shown that multispectral hand images (thermal and visible) can be useful in gender recognition.

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Section: Gender Recognition Using Visible Imagesmentioning

confidence: 99%

Gender Recognition Based on Hand Thermal Characteristic

Prihodova¹

2022

AIP

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11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Afifi

2019

Multimed Tools Appl

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113

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The human hand possesses distinctive features which can reveal gender information. In addition, the hand is considered one of the primary biometric traits used to identify a person. In this work, we propose a large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification. Using this dataset, a convolutional neural network (CNN) can be trained effectively for the gender recognition task. Based on this, we design a two-stream CNN to tackle the gender recognition problem. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for the biometric identification task. We show that the dorsal side of hand images, captured by a regular digital camera, convey effective distinctive features similar to, if not better, those available in the palmar hand images. To facilitate access to the proposed dataset and replication of our experiments, the dataset, trained CNN models, and Matlab source code are available at (https://goo.gl/rQJndd).

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