Dr. Ayman A. Abaza Profile

Dr. Ayman A. Abaza

Senior Scientist at West Virginia High Technology Consortium

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 29 May 2014 Paper

Multimodal biometrics system based on face profile and ear

Iman Youssef, Ayman Abaza, Mohamed Rasmy, Ahmed Badawi

Proceedings Volume 9075, 907506 (2014) https://doi.org/10.1117/12.2050159

KEYWORDS: Ear, Image fusion, Biometrics, Databases, Binary data, Facial recognition systems, Principal component analysis, Image segmentation, Feature extraction, Multimodal biometric systems

Read Abstract +

Proceedings Article | 31 May 2013 Paper

Ear recognition: a complete system

Ayman Abaza, Mary Ann Harrison

Proceedings Volume 8712, 87120N (2013) https://doi.org/10.1117/12.2015946

KEYWORDS: Ear, Databases, Feature extraction, Binary data, Image segmentation, Principal component analysis, Biometrics, Facial recognition systems, Wavelet transforms, Error analysis

Read Abstract +

Proceedings Article | 18 May 2012 Paper

Human ear detection in the thermal infrared spectrum

Ayman Abaza, Thirimachos Bourlai

Proceedings Volume 8354, 83540X (2012) https://doi.org/10.1117/12.919285

KEYWORDS: Ear, Mid-IR, Thermography, Cameras, Image segmentation, Databases, Infrared radiation, Image resolution, Spatial resolution, Environmental sensing

Read Abstract +

In this paper the problem of human ear detection in the thermal infrared (IR) spectrum is studied in order to illustrate the advantages and limitations of the most important steps of ear-based biometrics that can operate in day and night time environments. The main contributions of this work are two-fold: First, a dual-band database is assembled that consists of visible and thermal profile face images. The thermal data was collected using a high definition middle-wave infrared (3-5 microns) camera that is capable of acquiring thermal imprints of human skin. Second, a fully automated, thermal imaging based ear detection method is developed for real-time segmentation of human ears in either day or night time environments. The proposed method is based on Haar features forming a cascaded AdaBoost classifier (our modified version of the original Viola-Jones approach¹ that was designed to be applied mainly in visible band images). The main advantage of the proposed method, applied on our profile face image data set collected in the thermal-band, is that it is designed to reduce the learning time required by the original Viola-Jones method from several weeks to several hours. Unlike other approaches reported in the literature, which have been tested but not designed to operate in the thermal band, our method yields a high detection accuracy that reaches ~ 91.5%. Further analysis on our data set yielded that: (a) photometric normalization techniques do not directly improve ear detection performance. However, when using a certain photometric normalization technique (CLAHE) on falsely detected images, the detection rate improved by ~ 4%; (b) the high detection accuracy of our method did not degrade when we lowered down the original spatial resolution of thermal ear images. For example, even after using one third of the original spatial resolution (i.e. ~ 20% of the original computational time) of the thermal profile face images, the high ear detection accuracy of our method remained unaffected. This resulted also in speeding up the detection time of an ear image from 265 to 17 milliseconds per image. To the best of our knowledge this is the first time that the problem of human ear detection in the thermal band is being investigated in the open literature.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years