Multimodal Age and Gender Classification Using Ear and Profile Face Images

Yaman, Dogucan; Eyiokur, Fevziye Irem; Ekenel, Hazım Kemal

doi:10.1109/cvprw.2019.00296

Cited by 35 publications

(20 citation statements)

References 28 publications

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“…In order to improve the accuracy of age estimation, many multi-featurebased methods were proposed in recently years. For example, Yaman et al [9] proposed a multimodal age estimation method by combining ear and profile face images. Antipov et al [10] presented a deep learning model for age estimation by fusing the general and children-specialized features.…”

Section: Multi-feature Extraction For Age Estimationmentioning

confidence: 99%

Towards Efficient Age Estimation by Embedding Potential Gender Features

Deng

Zhang

Teng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Human age estimation from face image has drawn increasing research attention due to its many meaningful applications such as demographics analysis and surveillance monitoring. However, most existing methods directly extract age-specific features for age estimation and ignore age-related gender information. In this paper, we propose a simplified deep learning network for age estimation by simultaneously learning aging and potential gender features. Specifically, we first learn the potential gender information from face images. Then, we employ a two-stream convolutional neural network to simultaneously learn and concatenate the aging and gender latent appearance features. Third, we feed the multi-type features into a compact convolution network, named AgeNetwork, to further learn the age-specific features. Finally, we use a deep regression function to estimate the detailed ages. Extensive experimental results demonstrate the promising effectiveness and efficiency of our proposed method in comparison with state-of-the-arts.

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Section: Multi-feature Extraction For Age Estimationmentioning

confidence: 99%

Towards Efficient Age Estimation by Embedding Potential Gender Features

Deng

Zhang

Teng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Yamen et al [17] explore three approaches to fuse the profile and ear, namely, spatial fusion, intensity fusion, and channel fusion. In spatial fusion, they concatenate profile face and ear images side-by-side.…”

Section: Face-and Ear-based Multimodal Biometricsmentioning

confidence: 99%

An Overview of Multimodal Biometrics Using the Face and Ear

Huang

Wang

et al. 2020

Mathematical Problems in Engineering

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In the recent years, we have witnessed the rapid development of face recognition, though it is still plagued by variations such as facial expressions, pose, and occlusion. In contrast to the face, the ear has a stable 3D structure and is nearly unaffected by aging and expression changes. Both the face and ear can be captured from a distance and in a nonintrusive manner, which makes them applicable to a wider range of application domains. Together with their physiological structure and location, the ear can readily serve as supplement to the face for biometric recognition. It has been a trend to combine the face and ear to develop nonintrusive multimodal recognition for improved accuracy, robustness, and security. However, when either the face or the ear suffers from data degeneration, if the fusion rule is fixed or with inferior flexibility, a multimodal system may perform worse than the unimodal system using only the modality with better quality sample. The biometric quality-based adaptive fusion is an avenue to address this issue. In this paper, we present an overview of the literature about multimodal biometrics using the face and ear. All the approaches are classified into categories according to their fusion levels. In the end, we pay particular attention to an adaptive multimodal identification system, which adopts a general biometric quality assessment (BQA) method and dynamically integrates the face and ear via sparse representation. Apart from a refinement of the BQA and fusion weights selection, we extend the experiments for a more thorough evaluation by using more datasets and more types of image degeneration.

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“…The complete information of identity from ear images, utilizing soft biometric traits [6], such as gender, can be supplementary. Ear images have previously been used for identifying humans [5][11][24][25] [26], classifying gender [7][8][9] [10], and verifying kinship [13].…”

Section: Introductionmentioning

confidence: 99%

“…Yaman et. al [10] reports excellent gender identification 98% and with fusion with face profiles can reach very high accuracy (>99%). This is the current state-of-the-art in ear biometrics for gender classification.…”

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confidence: 97%

On Distinctiveness and Symmetry in Ear Biometrics

Dong

Nixon

Mahmoodi

2021

IEEE Trans. Biom. Behav. Identity Sci.

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Previous works show that human ears can be used for identification, gender classification, and kinship verification and have investigated whether there is a symmetry between a person's ears; however, the symmetry performances have been less than satisfactory. Our paper extends the analysis of gender classification on ear images and analyses bilateral symmetry of human ears, in both cases aiming to determine the ear parts from which recognition is derived. We use model-based analysis and deep learning methods to capitalize on structure and performance, respectively. We consider the rotation of ear images under an affine transformation, by modelling the ear as a flat plane attached to the head. We address the question as to whether it is possible that given an image of one ear, a person can then be recognized from their other ear. Such a symmetrybased strategy could reduce constraints on applications of ear biometrics. We show that it is possible to recognise the gender with a 90.9% success rate and that the ear rim (the upper helix and lobe) dominates performance. To investigate symmetry, we compare one ear with a mirrored version of the other ear and achieve 93.1% CCR, which is the current state-of-the-art, with important regions different from those determined for gender. To extend the analysis we construct two groups of images, one of which contains both ears from the same subject and the other contains two ears from different subjects. The 100% CCR confirms the existence of symmetry between a subject's ears. By these approaches we show that there is actually a high chance that there exists symmetry between a person's ears and that it would be prudent for recognition systems to concentrate on the inner ear rather than the outer ear.

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Multimodal Age and Gender Classification Using Ear and Profile Face Images

Cited by 35 publications

References 28 publications

Towards Efficient Age Estimation by Embedding Potential Gender Features

Towards Efficient Age Estimation by Embedding Potential Gender Features

An Overview of Multimodal Biometrics Using the Face and Ear

On Distinctiveness and Symmetry in Ear Biometrics

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