3D Facial landmark detection under large yaw and expression variations

A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of principal curvature values of a 3D object’s surface, and spin images, local descriptors of the object’s 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data

Bidirectional relighting for 3D-aided 2D face recognition

In this paper, we present a new method for bidirectional relighting for 3D-aided 2D face recognition under large pose and illumination changes. During subject enrollment, we build subject-specific 3D annotated models by using the subjects' raw 3D data and 2D texture. During authentication, the probe 2D images are projected onto a normalized image space using the subject-specific 3D model in the gallery. Then, a bidirectional relighting algorithm and two similarity metrics (a view-dependent complex wavelet structural similarity and a global similarity) are employed to compare the gallery and probe. We tested our algorithms on the UHDB11 and UHDB12 databases that contain 3D data with probe images under large lighting and pose variations. The experimental results show the robustness of our approach in recognizing faces in difficult situations

Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice

Computer graphics and vision methods for the reconstruction, representation and retrieval of 3D objects with biometric applications

The increase of availability of three dimensional objects, widens their use by computer graphics, computer vision and biometrics applications. Three dimensional data increase the available information about the object, but in the same times introduces new challenges. On most applications where three dimensional object databases are utilized, the goal is their retrieval, which is the ability to compare and categorize these objects. The retrieval procedure requires the optimal reconstruction of the objects as well as the efficient representation of the three dimensional information. In this dissertation, the general problem of managing three dimensional information is tackled, for the reconstruction, the representation and the retrieval of three dimensional objects. Initially, a new method for the reconstruction of three dimensional objects based on Integral Photography is presented. This method extracts the three dimensional information of real-life objects from a grid of elemental images. Additionally, two three dimensional object representations are presented that are utilized by two novel retrieval methods. The reduction of dimensionality is the common feature of both representations. The first representation, based on depth images, is suitable for inter-class object retrieval while the second, based on geometry images, is suitable for intra-class object retrieval. The geometry images are created using a subdivision-based deformable model framework. The intra-class object retrieval method is further specialized and applied in the field of biometrics, since the human body’s geometry is a strong biometric. More specifically, three dimensional information is used to perform face and ear recognition. The performance of the proposed method is evaluated using the largest publicly available biometric databases. The face recognition results that are presented are considered state-of-the-art worldwide.Καθώς η διαθεσιμότητα των τρισδιάστατων αντικειμένων αυξάνεται, ευρύνεται και η χρήση τους από εφαρμογές γραφικών, τεχνητής όρασης και βιομετρίας. Η χρήση τρισδιάστατων δεδομένων αυξάνει την διαθέσιμη πληροφορία για το αντικείμενο αλλά ταυτόχρονα εισάγει νέες προκλήσεις. Στις περισσότερες εφαρμογές όπου χρησιμοποιούνται βάσεις δεδομένων τρισδιάστατων αντικειμένων το ζητούμενο είναι η ανάκτηση, η δυνατότητα δηλαδή σύγκρισης και κατηγοριοποίησης των αντικειμένων. Η διαδικασία της ανάκτησης προϋποθέτει την ορθή ανακατασκευή των αντικειμένων και την αποδοτική αναπαράσταση της τρισδιάστατης πληροφορίας. Σε αυτήν την διατριβή αντιμετωπίζεται συνολικά το θέμα της διαχείρισης της τρισδιάστατης πληροφορίας για την ανακατασκευή, αναπαράσταση και ανάκτηση τρισδιάστατων αντικειμένων. Αρχικά παρουσιάζεται μια νέα μέθοδος για την ανακατασκευή τρισδιάστατων αντικειμένων με την χρήση ολοκληρωτικής φωτογράφησης. Με την μέθοδο αυτή εξάγεται τρισδιάστατη πληροφορία για πραγματικά αντικείμενα μέσα από μια σειρά από στοιχειώδης εικόνες. Στη συνέχεια παρουσιάζονται μέθοδοι για την αναπαράσταση των τρισδιάστατων αντικειμένων οι οποίες χρησιμοποιούνται από δυο νέες μεθόδους ανάκτησης. Ο κοινός παρονομαστής και των δύο είναι η μείωση διάστασης της τρισδιάστατης πληροφορίας. Η πρώτη μέθοδος είναι κατάλληλη για ανάκτηση αντικειμένων διαφορετικών κλάσεων και χρησιμοποιεί την αναπαράσταση με εικόνες βάθους. Η δεύτερη μέθοδος είναι κατάλληλη για ανάκτηση αντικειμένων όμοιας κλάσης και χρησιμοποιεί την αναπαράσταση με γεωμετρικές εικόνες. Οι γεωμετρικές εικόνες παράγονται με την χρήση παραμορφόσιμων μοντέλων βασισμένων σε επιφάνειες υποδιαίρεσης. Η μέθοδος ανάκτησης αντικειμένων όμοιας κλάσης εξειδικεύεται περαιτέρω και εφαρμόζεται στον τομέα της βιομετρίας, καθώς η γεωμετρία του ανθρώπινου σώματος αποτελεί ισχυρό βιομετρικό χαρακτηριστικό. Συγκεκριμένα, τρισδιάστατη πληροφορία χρησιμοποιείται για να γίνει αναγνώριση προσώπου και αυτιού. Η ακρίβεια της προτεινόμενης μεθόδου αξιολογείται χρησιμοποιώντας τις εκτενέστερες διαθέσιμες βάσεις δεδομένων. Τα αποτελέσματα αναγνώρισης προσώπου που παρουσιάζονται σε αυτήν την διατριβή είναι τα κορυφαία σε παγκόσμιο επίπεδο

Bidirectional relighting for 3D-aided 2D face recognition

In this paper, we present a new method for bidirectional relighting for 3D-aided 2D face recognition under large pose and illumination changes. During subject enrollment, we build subject-specific 3D annotated models by using the subjects' raw 3D data and 2D texture. During authentication, the probe 2D images are projected onto a normalized image space using the subject-specific 3D model in the gallery. Then, a bidirectional relighting algorithm and two similarity metrics (a view-dependent complex wavelet structural similarity and a global similarity) are employed to compare the gallery and probe. We tested our algorithms on the UHDB11 and UHDB12 databases that contain 3D data with probe images under large lighting and pose variations. The experimental results show the robustness of our approach in recognizing faces in difficult situations. ©2010 IEEE.</p