Automatic identity recognition from ear images represents an active field of research within
the biometric community. The ability to capture ear images from a distance and in a covert …

Human recognition with biometrics is a rapidly emerging area of computer vision. Compared
to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the …

Identity recognition using local features extracted from ear images has recently attracted a
great deal of attention in the intelligent biometric systems community. The rich and reliable …

This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely
AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear …

Automatic identity recognition from ear images is an active research topic in the biometric
community. The ability to secretly acquire images of the ear remotely and the stability of the …

This paper presents ear recognition models constructed with Deep Residual Networks
(ResNet) of various depths. Due to relatively limited amounts of ear images we propose …

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 …

Recent times are witnessing greater influence of Artificial Intelligence (AI) on identification of
subjects based on biometrics. Traditional biometric recognition algorithms, which were …

Extraction and description of image features is an active research topic and important for
several applications of computer vision field. This paper presents a new noise-tolerant and …

The discriminative ability of geometric features can be well supported by empirical studies in
ear recognition. Recently, a number of methods have been suggested for geometric feature …