Search Results (2)

In this paper, we assessed the short-term effects of 0.01% atropine eye drops on anterior segment parameters by performing ocular biometry using a swept-source anterior segment optical coherence tomography system (AS-OCT). We recruited 17 healthy volunteers (10 men and 7 women aged 24–35 years) with no history of eye disease. Participants without accommodative demand demonstrated significant mydriasis 1 h after the atropine instillation (4.58 ± 0.77 to 5.41 ± 0.83 mm). Pupil diameters with a 5 diopter (D) accommodative stimulus at 1 h (4.70 ± 1.13 mm) and 24 h (4.05 ± 1.06 mm) after atropine instillation were significantly larger than those at baseline (3.71 ± 0.84 mm). Barring pupil diameter, no other biometric parameters significantly changed at any point in time after atropine instillation without accommodative demand. However, with an accommodative stimulus, anterior chamber depth (ACD) at 1 h and posterior curvature of the lens at 1 and 24 h were both significantly larger than those before atropine instillation. Using AS-OCT, we detected a slight decrease in the accommodation response of ocular biometric components evoked by 0.01% atropine instillation. Morphologically, our measurements suggested a change in the ACD and horizontal radius of the lens’ posterior surface curvatures due to the subtle reduction of accommodation. Full article

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach. Full article