Perceiving and recognizing three-dimensional forms
Perceiving and recognizing three-dimensional forms
Eye finding via face detection for a foveated, active vision system
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust real-time face tracker for cluttered environments
Computer Vision and Image Understanding
Handbook of Face Recognition
Face Verification Using GaborWavelets and AdaBoost
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Face Parts in Gender Recognition
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
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Family members have close facial resemblances to one another; especially for certain specific parts of the face but the resemblance part differ from family to family. However, we have no problem in identifying such facial resemblances to guess the family relationships. This paper attempts to develop such human capability in computers through measurements of the resemblance of each facial patch to classify family members. To achieve this goal, family datasets are collected. A modified Golden Ratio Mask is implemented to guide the facial patches. Features of each facial patch are selected, analyzed by an individual classifier and the importance of each patch is extracted to find the set of most informative patches. To evaluate the performance, various scenarios where different members of the family are absent from training but present in testing are tested to classify the family members. Results obtained show that we can achieve up to 98% average accuracy on the collected dataset.