DIAST variability illuminated thermal and visible ear images datasets

Ariffin, Syed Mohd Zahid Syed Zainal; Jamil, Nursuriati; Rahman, Puteri Norhashimah Megat Abdul

doi:10.1109/spa.2016.7763611

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…In our research, we worked with the DIAST dataset, as explained by Ariffin et al (2016). This dataset is like a treasure trove containing both thermal and visible ear images-2200 in total, from 55 different folks.…”

Section: Datasetmentioning

confidence: 99%

Exploring Ear Recognition: Image Fusion and Deep Learning with Thermal and Visible Images

2024

IJSREM

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This pioneering study delves into the realm of ear recognition, introducing an innovative methodology that combines thermal and visible ear images using sophisticated multiresolution analysis, including discrete wavelet, ridgelet, and curvelet transforms. Our tailored deep learning model, uniquely designed for ear recognition, showcases outstanding results, with the complex-valued curvelet transform paired with thermal images achieving an impressive 96.82% recognition rate—outperforming all other methods. Notably, this research underscores the transformative impact of multi-source data fusion on elevating the effectiveness of ear recognition systems. Key Words: ridgelet, curvelet, effectiveness, multiresolution, pioneering

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Section: Datasetmentioning

confidence: 99%

Exploring Ear Recognition: Image Fusion and Deep Learning with Thermal and Visible Images

2024

IJSREM

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“…In this study, it was used the DIAST dataset, which includes both thermal and visible ear images, as detailed by Ariffin et al (2016). This dataset comprises a total of 2200 ear images, representing 55 different individuals.…”

Section: Datasetmentioning

confidence: 99%

Image Fusion and Deep Learning Based Ear Recognition Using Thermal and Visible Images

CİHAN,

CEYLAN

2023

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

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Advances in imaging and deep learning have fueled interest in ear biometrics, as the structure of the ear offers unique identification features. Thermal and visible ear images capture different aspects of these features. Thermal images are light-independent, and visible images excel at capturing texture details. Combining these images creates more feature-rich composite images. This study examines the fusion of thermal and visible ear images taken under varying lighting conditions to enhance automatic ear recognition. The image fusion process involved three distinct multiresolution analysis methods: discrete wavelet transform, ridgelet transform, and curvelet transform. Subsequently, a specially designed deep learning model was used for ear recognition. The results of this study reveal that employing the complex-valued curvelet transform and thermal images achieved an impressive recognition rate of 96.82%, surpassing all other methods. Conversely, visible images exhibited the lowest recognition rate of 75.00%, especially in low-light conditions. In conclusion, the fusion of multiple data sources significantly enhances ear recognition effectiveness, and the proposed model consistently achieves remarkable recognition rates even when working with a limited number of fused ear images.

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“…The lux meter sensor is placed directly under the ear to ensure an accurate reading. Details of the image acquisition can be found in our previous work [24]. For the uniform illumination variance, we managed to collect ear images with illuminations ranging from 2 lux to 10700 lux from 55 subjects.…”

Section: The Uniform Illuminant Invariant Ear Image Datasetmentioning

confidence: 99%

Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?

Jamil¹,

Almisreb²,

Ariffin³

et al. 2018

IJEECS

Self Cite

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Current deep convolution neural network (CNN) has shown to achieve superior performance on a number of computer vision tasks such as image recognition, classification and object detection. The deep network was also tested for view-invariance, robustness and illumination invariance. However, the CNN architecture has thus far only been tested on non-uniform illumination invariant. Can CNN perform equally well for very underexposed or overexposed images or known as uniform illumination invariant? This is the gap that we are addressing in this paper. In our work, we collected ear images under different uniform illumination conditions with lumens or lux values ranging from 2 lux to 10,700 lux. A total of 1,100 left and right ear images from 55 subjects are captured under natural illumination conditions. As CNN requires considerably large amount of data, the ear images are further rotated at every 5o angles to generate 25,300 images. For each subject, 50 images are used as validation/testing dataset, while the remaining images are used as training datasets. Our proposed CNN model is then trained from scratch and validation and testing results showed recognition accuracy of 97%. The results showed that 100% accuracy is achieved for images with lumens ranging above 30 but having problem with lumens less than 10 lux

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DIAST variability illuminated thermal and visible ear images datasets

Cited by 8 publications

References 8 publications

Exploring Ear Recognition: Image Fusion and Deep Learning with Thermal and Visible Images

Exploring Ear Recognition: Image Fusion and Deep Learning with Thermal and Visible Images

Image Fusion and Deep Learning Based Ear Recognition Using Thermal and Visible Images

Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?

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