A novel geometric feature extraction method for ear recognition

Omara, Ibrahim; Li, Feng; Zhang, Hongzhi; Zuo, Wangmeng

doi:10.1016/j.eswa.2016.08.035

Cited by 59 publications

(21 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the DMS-BSIF representation outperforms the improved BSIF descriptor by 0.69%, 0.09% and 0.77% for IITD-1, IITD-2 and USTB-1 databases, respectively. However, the performance of DMS-BSIF is lower than the feature used in Omara et al (2016) for IITD-1 database. As the ear consists of several geometric structures such as curvatures, the proposed approaches of Basit and Shoaib (2014) and Omara et al (2016) based on geometric measurements give interesting results in terms of accuracy.…”

Section: Experiments #3mentioning

confidence: 87%

A novel discriminant multiscale representation for ear recognition

Doghmane

Boukrouche

Boubchir

2019

IJBM

View full text Add to dashboard Cite

This paper proposes a novel representation for ear recognition. It introduces a new alternative of binarised statistical image features based on multiscale framework. The proposed representation allows capturing the image content at multiple resolutions. The recognition accuracy can be enhanced by the following steps. First, for a given ear image, a set of multiscale response images are derived from the bank of binarised statistical image features (B-BSIF) filter. Second, the obtained response images are summarised by concatenating their histograms, which are obtained at each scale. Finally, a discriminative ear image representation is build by projecting the above mentioned histograms into a linear discriminant analysis subspace. The proposed representation is applied on three public databases: IIT Delhi-1, IIT Delhi-2 and USTB. The obtained recognition accuracy confirms its performance than the recent existing methods.

show abstract

Section: Experiments #3mentioning

confidence: 87%

A novel discriminant multiscale representation for ear recognition

Doghmane

Boukrouche

Boubchir

2019

IJBM

View full text Add to dashboard Cite

show abstract

“…These techniques are classified into four main categories based on the employed feature extraction method [22]. The first category includes geometric techniques that consider the geometrical parts of the ear as discriminating features [23][24][25]. A common approach is to apply an edge detector to describe edges from the ear image.…”

Section: Related Workmentioning

confidence: 99%

Handcrafted versus CNN Features for Ear Recognition

et al. 2019

View full text Add to dashboard Cite

Ear recognition is an active research area in the biometrics community with the ultimate goal to recognize individuals effectively from ear images. Traditional ear recognition methods based on handcrafted features and conventional machine learning classifiers were the prominent techniques during the last two decades. Arguably, feature extraction is the crucial phase for the success of these methods due to the difficulty in designing robust features to cope with the variations in the given images. Currently, ear recognition research is shifting towards features extracted by Convolutional Neural Networks (CNNs), which have the ability to learn more specific features robust to the wide image variations and achieving state-of-the-art recognition performance. This paper presents and compares ear recognition models built with handcrafted and CNN features. First, we experiment with seven top performing handcrafted descriptors to extract the discriminating ear image features and then train Support Vector Machines (SVMs) on the extracted features to learn a suitable model. Second, we introduce four CNN based models using a variant of the AlexNet architecture. The experimental results on three ear datasets show the superior performance of the CNN based models by 22%. To further substantiate the comparison, we perform visualization of the handcrafted and CNN features using the t-distributed Stochastic Neighboring Embedding (t-SNE) visualization technique and the characteristics of features are discussed. Moreover, we conduct experiments to investigate the symmetry of the left and right ears and the obtained results on two datasets indicate the existence of a high degree of symmetry between the ears, while a fair degree of asymmetry also exists.

show abstract

“…Recently, an approach using geometric information of the ear has been presented in Omara, Li, Zhang, and Zuo (). The proposed method depends on the shape of the ear, which involves image preprocessing using Gaussian filter and ear helix detection by Canny edge operator, which consists of both the outer and inner helixes.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, an approach using geometric information of the ear has been presented in Omara, Li, Zhang, and Zuo (2016 Rathore, Prakash, and Gupta (2013) to extract features from the enhanced images of ear and profile face. The recognition performance was improved by fusion of ear and profile face.…”

Section: Related Workmentioning

confidence: 99%

On the use of ear and profile faces for distinguishing identical twins and nontwins

2019

View full text Add to dashboard Cite

This study aims to measure the efficiency of ear and profile face in distinguishing identical twins under identification and verification modes. In addition, to distinguish identical twins by ear and profile face separately, we propose to fuse these traits with all possible binary combinations of left ear, left profile face, right ear, and right profile face. Fusion is implemented by score‐level fusion and decision‐level fusion techniques in the proposed method. Additionally, feature‐level fusion is used for comparison. All experiments in this paper are also implemented on nontwins individuals, and the recognition performance of twins and nontwins are compared. Local binary patterns, local phase quantization, and binarized statistical image features approaches are used as texture‐based descriptors for feature extraction process. Images under controlled and uncontrolled lighting are tested. Ear and profile images from ND‐TWINS‐2009‐2010 dataset are used in the experiments. The experimental results show that the proposed method is more accurate and reliable than using ear or profile face images separately. The performance of the proposed method for recognizing identical twins as recognition rate is 100% and 99.45%, and equal error rates are 0.54% and 1.63% in controlled and uncontrolled illumination conditions, respectively.

show abstract

A novel geometric feature extraction method for ear recognition

Cited by 59 publications

References 22 publications

A novel discriminant multiscale representation for ear recognition

A novel discriminant multiscale representation for ear recognition

Handcrafted versus CNN Features for Ear Recognition

On the use of ear and profile faces for distinguishing identical twins and nontwins

Contact Info

Product

Resources

About