Machine Learning for 3D Face Recognition using off-the-shelf sensors

The human brain is inherently hardwired to read psychological state before identity, hence its robustness towards the dynamic nature of faces and viewpoint changes. The novelty of this research consists in learning abstract atomical representations of shape cues, thus having the potential to solve multiple classification problems since a depiction unit can have multiple task-specific attributes. Machine learning versatility is paramount and as such multiple ensembles of varying complexity are trained based on unsupervised specialization of experts. A dataset of 18 individuals was recorded based on different variances in pose, expression and distance to the Kinect sensor. Using 3D object feature descriptors, the performance for face recognition is studied over 36 variance-specific pair tests, concluding that simple ensembles outperform complex ones, since the utility of each expert is highly dependent on the sampling resolution, distance metric and type of features. The methodology is robust towards occlusions and the performance can reach accuracies up to 90% depending on the complexity of the dataset, despite that there is no human supervision for generating the face region labels.

Ensemble methods for robust 3D face recognition using commodity depth sensors

In this paper we introduce a new dataset and pose invariant sampling method and describe the ensemble methods used for recognizing faces in 3D scenes, captured using commodity depth sensors. We use the 3D SIFT key point detector to take advantage of the similarities between faces, which leads to a set of points of interest based on the curvature of the face. For all key points, features are extracted using a 3D feature descriptor. Then, a variable-sized amount of features are generated per each 3D face image. The first ensemble method we constructed uses a K-nearest neighbors classifier to classify each key point-sampled feature vector as belonging to one of the subjects recorded in our dataset. All votes over all key points are combined. In the second ensemble technique, the key points are clustered with K-means, using the feature vectors and approximated sampling positions relative to the face. This leads to a set of experts that specialize for a specific region. Then a K-nearest neighbors classifier is trained on the examples falling in each expert's specialized region. Finally, for a new 3D face image, votes from all experts are combined in a sum ensemble technique to categorize the 3D face. We also introduce 6 new "real world" datasets with different variances: 3 types of 3D rotations, distance to sensor, expressions, and an all-in-one dataset. The results show very high cross validation accuracies for the same type of variance. In addition, 36 variance specific pair-Tests in which the system is trained on one dataset and tested on a completely different dataset also show encouraging results