The investigation of the effects of observers' gender in personal identification from auricle morphology

Yavuz, Mehmet Sunay; Tatlısumak, Ertuğrul; Özyurt, Beyhan Cengiz; Aşırdizer, Mahmut

doi:10.5505/adlitip.2013.08216

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…The first component in the sequence is taken from the last layer of the encoder and fed to the head classifier as shown in (5) to predict the class label.…”

Section: Vision Transformer Encodermentioning

confidence: 99%

“…Due to its unique and relatively stable characteristics, the ear functions as a valuable biometric identifier in both biometric research and forensic medicine [4]. In contrast to the face, which can be affected by factors such as facial expressions, facial hair, and cosmetics, the ear's appearance is stable, making it an advantageous identifier [5]. Particularly, the earlobe stands out as a distinguishing characteristic frequently used in forensic investigations, and it continues to change over time, providing additional identification clues [6].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, numerous studies have concentrated on the use of ear images for identification purposes [3][4][5][6][7][8][9]. In contrast, the research on extracting sensitive biometric features from ear images, such as age, has been relatively limited.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Gender Identification Using Ear Images

Kılıç,

Doğan

2023

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The classification of an individual as male or female is a significant issue with several practical implications. In recent years, automatic gender identification has garnered considerable interest because of its potential applications in e-commerce and the accumulation of demographic data. Recent observations indicate that models based on deep learning have attained remarkable success in a variety of problem domains. In this study, our aim is to establish an end-to-end model that capitalizes on the strengths of competing convolutional neural network (CNN) and vision transformer (ViT) models. To accomplish this, we propose a novel approach that combines the MobileNetV2 model, which is recognized for having fewer parameters than other CNN models, with the ViT model. Through rigorous evaluations, we have compared our proposed model with other recent studies using the accuracy metric. Our model attained state-of-the-art performance with a remarkable score of 96.66% on the EarVN1.0 dataset, yielding impressive results. In addition, we provide t-SNE results that demonstrate our model's superior learning representation. Notably, the results show a more effective disentanglement of classes.

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“…The first component in the sequence is taken from the last layer of the encoder and fed to the head classifier as shown in (5) to predict the class label.…”

Section: Vision Transformer Encodermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Based Gender Identification Using Ear Images

Kılıç,

Doğan

2023

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“…For example, compared to facial appearance, which is influenced by changes in facial expression, facial hair or makeup, ear appearance is relatively constant. Auricular is also a defining feature of face [4]. Among the ear parts, earlobe is the most frequently used part in forensic cases.…”

Section: Introductionmentioning

confidence: 99%

Age and gender classification from ear images

Yaman

Eyiokur

Sezgin

et al. 2018

2018 International Workshop on Biometrics and Forensics (IWBF)

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In this paper, we present a detailed analysis on extracting soft biometric traits, age and gender, from ear images. Although there have been a few previous work on gender classification using ear images, to the best of our knowledge, this study is the first work on age classification from ear images. In the study, we have utilized both geometric features and appearance-based features for ear representation. The utilized geometric features are based on eight anthropometric landmarks and consist of 14 distance measurements and two area calculations. The appearance-based methods employ deep convolutional neural networks for representation and classification. The well-known convolutional neural network models, namely, AlexNet, VGG-16, GoogLeNet, and SqueezeNet have been adopted for the study. They have been fine-tuned on a large-scale ear dataset that has been built from the profile and close-to-profile face images in the Multi-PIE face dataset. This way, we have performed a domain adaptation. The updated models have been fine-tuned once more time on the small-scale target ear dataset, which contains only around 270 ear images for training. According to the experimental results, appearance-based methods have been found to be superior to the methods based on geometric features. We have achieved 94% accuracy for gender classification, whereas 52% accuracy has been obtained for age classification. These results indicate that ear images provide useful cues for age and gender classification, however, further work is required for age estimation.

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The investigation of the effects of observers' gender in personal identification from auricle morphology

Abstract: Yavuz MS, Tatlısumak E, Özyurt B, Aşırdizer M ORIGINAL ARTICLEYavuz MS, Tatlısumak E, Ozyurt B, Asırdizer M. The investigation of the effects of observer gender in personal identification from auricle morphology.

Cited by 2 publications

References 7 publications

Deep Learning Based Gender Identification Using Ear Images

Deep Learning Based Gender Identification Using Ear Images

Age and gender classification from ear images

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