3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications

Deep Shape Representations for 3D Object Recognition

Deep learning is a rapidly growing discipline that models high-level features in data as multilayered neural networks. The recent trend toward deep neural networks has been driven, in large part, by a combination of affordable computing hardware, open source software, and the availability of pre-trained networks on large-scale datasets. In this thesis, we propose deep learning approaches to 3D shape recognition using a multilevel feature learning paradigm. We start by comprehensively reviewing recent shape descriptors, including hand-crafted descriptors that are mostly developed in the spectral geometry setting and also the ones obtained via learning-based methods. Then, we introduce novel multi-level feature learning approaches using spectral graph wavelets, bag-of-features and deep learning. Low-level features are first extracted from a 3D shape using spectral graph wavelets. Mid-level features are then generated via the bag-of-features model by employing locality-constrained linear coding as a feature coding method, in conjunction with the biharmonic distance and intrinsic spatial pyramid matching in a bid to effectively measure the spatial relationship between each pair of the bag-offeature descriptors. For the task of 3D shape retrieval, high-level shape features are learned via a deep auto-encoder on mid-level features. Then, we compare the deep learned descriptor of a query shape to the descriptors of all shapes in the dataset using a dissimilarity measure for 3D shape retrieval. For the task of 3D shape classification, mid-level features are represented as 2D images in order to be fed into a pre-trained convolutional neural network to learn high-level features from the penultimate fully-connected layer of the network. Finally, a multiclass support vector machine classifier is trained on these deep learned descriptors, and the classification accuracy is subsequently computed. The proposed 3D shape retrieval and classification approaches are evaluated on three standard 3D shape benchmarks through extensive experiments, and the results show compelling superiority of our approaches over state-of-the-art methods