An Ear Recognition Method Based on Rotation Invariant Transformed DCT

Human recognition systems have gained great importance recently in a wide range of applications like access, control, criminal investigation and border security. Ear is an emerging biometric which has rich and stable structure and can potentially be implemented reliably and cost efficiently. Thus human ear recognition has been researched widely and made greatly progress. High recognition rates which are reported in most existing methods can be reached only under closely controlled conditions. Actually a slight amount of rotation and translation which is inescapable would be injurious for system performance. In this paper, a method that uses a transformed type of DCT is implemented to extract meaningful features from ear images. This algorithm is quite robust to ear rotation, translation and illumination. The proposed method is experimented on two popular databases, i.e. USTB II and IIT Delhi II, which achieves significant improvement in the performance in comparison to other methods with good efficiency based on LBP, DSIFT and Gabor. Also because of considering only important coefficients, this method is faster compared to other methods

Automatic human ear detection approach using modified adaptive search window technique

The human ear biometric recognition plays an important role in the forensics specialty and has significant impact for biometrician scientists and researchers. Actually, many ear recognition researches showed promised results, but some issues such as manual detection process, efficiency and robustness aren’t attained a certain level of maturity. Therefore, the enhancement developing approaches still continuous to achieve limited successes. We propose an efficient, reliable and simple automatic human ear detection approach. This approach implement two stages: preprocessing and ear landmarks detection. We utilized the image contrast, Laplace filter and Gaussian blurring techniques to made enhancement on all images (increasing the contrast, reduce the noisy and smoothing processes). After that, we highlighted the ear edges by using the Sobel edge detector and determining the only white pixels of ear edges by applying the image substation method. The improvement focused on using the modified adaptive search window (ASW) to detect the ear region. Furthermore, our approach is tested on Indian Institute of Technology (IIT) Delhi standard ear biometric public dataset. Experimental results presented a well average detection rate 96% for 493 image samples from 125 persons and computational time almost ≈ 0.485 seconds which is evaluated with other previous works

An automated ear identification system using Gabor filter responses

About some years ago, several biometric technologies are considered mature enough to be a new tool for security and ear-based person identification is one of these technologies. This technology provides a reliable, low cost and user-friendly viable solution for a range of access control applications. In this paper, we propose an efficient online personal identification system based on ear images. In this purpose, the identification algorithm aims to extract, for each ear, a specific set of features. Based on Gabor filter response, three ear features have been used in order to extract different and complementary information: phase, module and a combination of the real and imaginary parts. Using these features, several combinations are tested in the fusion phase in order to achieve an optimal multi-representation system which leads to a better identification accuracy. The obtained experimental results show that the system yields the best performance for identifying a person and it is able to provide the highest degree of biometrics-based system security