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Robust facial feature tracking under varying face pose and facial expression
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Attentiveness assessment in learning based on fuzzy logic analysis
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Eye localization in low and standard definition content with application to face matching
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Precise eye detection on frontal view face image
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Automatic eye detection using intensity filtering and K-means clustering
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Automatic eye detection in human faces using geostatistical functions and support vector machines
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Precise eye detection using discriminating HOG features
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Study of geostatistical functions applied to automatic eye detection
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Lucas-Kanade based entropy congealing for joint face alignment
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Supervised sparse patch coding towards misalignment-robust face recognition
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Precise localization of eye centers with multiple cues
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Face Recognition with Integrating Multiple Cues
Journal of Signal Processing Systems
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The accuracy of face alignment affects the performance of a face recognition system. Since face alignment is usually conducted using eye positions, an accurate eye localization algorithm is therefore essential for accurate face recognition. In this paper, we first study the impact of eye locations on face recognition accuracy, and then introduce an automatic technique for eye detection. The performance of our automatic eye detection technique is subsequently validated using FRGC 1.0 database. The validation shows that our eye detector has an overall 94.5% eye detection rate, with the detected eyes very close to the manually provided eye positions. In addition, the face recognition performance based on the automatic eye detection is shown to be comparable to that of using manually given eye positions.