Real-time iris detection on faces with coronal axis rotation.
ABSTRACT Real-time face and iris detection on video sequences is important in diverse applications such as, study of the eye function, drowsiness detection, virtual keyboard interfaces, face recognition and multimedia retrieval. In previous work we developed a non-invasive real time iris detection method consisting of three stages: coarse face detection, fine face detection and iris boundary detection. In this paper, iris detection is considered on faces with rotations in the coronal axis within the range -40° to 40°. It is shown that a line integral over the directional image as a function of the template rotation, has a maximum when the face and template coincide in rotation angle. The method was applied on 10 video sequences, with a total of 6470 frames, from different subjects rotating their faces in the coronal axis. Results of correct face detection on 8 video sequences were 100%, one reached 99.9% and one 98.2%. Results on correct iris detection are above 96% in 9 of the video sequences and one reached 77.8%. The method was implemented in real-time (30 frames per second) with a PC 1.8 GHz.
Conference Paper: Local matching Gabor entropy weighted face recognition.[Show abstract] [Hide abstract]
ABSTRACT: Face recognition has a wide range of possible applications in surveillance, access control, human computer interfaces and in electronic marketing and advertising for selected customers. Several models based on Gabor feature extraction have been proposed for face recognition with very good results on internationally available face databases. In this paper, we propose a methodological improvement to increase face recognition rate by selection and weighting Gabor jets by an entropy measure. We also propose improvements in the Borda count classification through a threshold to eliminate low score jets from the voting process to increase the face recognition rate. We show that combinations of weighting Gabor jets and threshold Borda yield the best results. We tested our methodological improvements on the FERET and the AR face databases. On the FERET database we reduce the total number of errors from 163 to 102 which is the highest score published up to date. The total number of errors in face recognition was reduced in 37%. On the AR database we also obtained important improvements and tested face images with illumination and gesticulation changes, and occlusions.Ninth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), Santa Barbara, CA, USA, 21-25 March 2011; 01/2011
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ABSTRACT: Various oxygenization methods are used in the treatment of respiratory failure in acute heart failure. Occasionally, after patients are stabilized by these ventilation methods, some maintain a degree of dyspnea or hypoxemia which does not improve and is unrelated to deterioration in the functional class or the need to optimize pharmacological treatment. High-flow oxygen systems administered via nasal cannula that are connected to heated humidifiers (HFT) are a good alternative for oxygenation, given that they are easy to use and have few complications. We studied a series of 5 patients with acute heart failure due to acute pulmonary edema with stable dyspnea or hypoxemia following noninvasive ventilation. All the patients were successfully treated with HFT, showing clinical and gasometric improvement and no complications or technical failures. We report our experience and discuss different aspects related to this oxygenation system.Revista Espa de Cardiologia 04/2011; 64(8):723-5. · 3.20 Impact Factor
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ABSTRACT: In this article we report a new method for gender classification from frontal face images using feature selection based on mutual information and fusion of features extracted from intensity, shape, texture, and from three different spatial scales. We compare the results of three different mutual information measures: minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), and conditional mutual information feature selection (CMIFS). We also show that by fusing features extracted from six different methods we significantly improve the gender classification results relative to those previously published, yielding 99.13% of the gender classification rate on the FERET database.International Journal of Optomechatronics 01/2012; 6(1):92-119. · 0.43 Impact Factor