Context-Aided Face Identification in Personal Photo Albums
Date Issued
2007
Date
2007
Author(s)
Chou, Liang-Yu
DOI
en-US
Abstract
One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. A large number of representative training samples are required to capture the considerable amounts of variations one face may undergo due to illumination, pose, expression, etc. In this paper, a classification framework is introduced as an attempt to alleviate the tedious effort spent on collecting training samples (i.e. speed up the annotation process). The user is required to annotate a small number of faces detected from the input photo set as different subjects (i.e. different clusters). Then, based on the technique of so-called generic learning, we classify the remaining faces into one of these clusters by comparing their pair-wise face similarities. A novel automatic face alignment algorithm is also devised for saving the user efforts on labeling eye coordinates. To boost the recognition performance in this one training sample application scenario, we extract context information as another cue for recognizing people. In addition, relevance feedback can also be added in a novel and intuitive way to iteratively refine the classifacation result. At the end of this article, experiments on real consumer photo albums are performed to demonstrate the effectiveness of the proposed framework.
Subjects
人臉分類
人臉辨識
人臉標註
單一訓練樣本的
face classification
face identification
face annotation
context-aided
one training sample
Type
thesis
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