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Using heterogeneous depth cameras and 3D scanners in 3D face verificationcauses variations in the resolution of the 3D point clouds. To solve thisissue, previous studies use 3D registration techniques. Out of these proposedtechniques, detecting points of correspondence is proven to be an efficientmethod given that the data belongs to the same individual. However, if the databelongs to different persons, the registration algorithms can convert the 3Dpoint cloud of one person to another, destroying the distinguishing featuresbetween the two point clouds. Another issue regarding the storage size of thepoint clouds. That is, if the captured depth image contains around 50 thousandpoints in the cloud for a single pose for one individual, then the storage sizeof the entire dataset will be in order of giga if not tera bytes. With thesemotivations, this work introduces a new technique for 3D point cloudsgeneration using a neural modeling system to handle the differences caused byheterogeneous depth cameras, and to generate a new face canonical compactrepresentation. The proposed system reduces the stored 3D dataset size, and ifrequired, provides an accurate dataset regeneration. Furthermore, the systemgenerates neural models for all gallery point clouds and stores these models torepresent the faces in the recognition or verification processes. For the probecloud to be verified, a new model is generated specifically for that particularcloud and is matched against pre-stored gallery model presentations to identifythe query cloud. This work also introduces the utilization of Siamese deepneural network in 3D face verification using generated model representations asraw data for the deep network, and shows that the accuracy of the trainednetwork is comparable all published results on Bosphorus dataset.

Neural Generative Models for 3D Faces with Application in 3D Texture Free Face Recognition