Feature fusion for facial landmark detection: A feature descriptors combination approach

Facial landmark detection is a crucial first step in facial analysis for biometrics and numerous other applications. However, it has proved to be a very challenging task due to the numerous sources of variation in 2D and 3D facial data. Although landmark detection based on descriptors of the 2D and 3D appearance of the face has been extensively studied, the fusion of such feature descriptors is a relatively under-studied issue. In this report, a novel generalized framework for combining facial feature descriptors is presented, and several feature fusion schemes are proposed and evaluated. The proposed framework maps each feature into a similarity score, combines the individual similarity scores into a resultant score, used to select the optimal solution for a queried landmark. The evaluation of the proposed fusion schemes for facial landmark detection clearly indicates that a quadratic distance to similarity mapping in conjunction with a root mean square rule for similarity fusion achieves the best performance in accuracy, efficiency, robustness and monotonicity

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

Ανίχνευση οροσήμων για την απρόσκοπη αναγνώριση προσώπου

In this dissertation a novel method for 3D landmark detection and pose estimation, suitable for both frontal and side 3D facial scans, is presented. The proposed method exploits 3D and 2D information by using local shape descriptors to extract candidate interest points that are subsequently identified and labeled as anatomical landmarks. Additionally, a novel generalized framework for combining facial feature descriptors that can be used for landmark detection is introduced. However, feature detection methods which use general purpose shape descriptors cannot identify and label the detected candidate landmarks. To this end, a 3D Facial Landmark Model (FLM) of facial anatomical landmarks is introduced. Candidate landmarks, irrespectively of the way they are generated, can be identified and labeled by matching them with the FLM. Finally, a novel method for unconstrained face recognition is introduced. It employs the 3D landmark detector to provide an initial pose estimation and to indicate occluded areas with missing data for each facial scan. Subsequently, a 3D Annotated Face Model (AFM) is registered and fitted to the scan using facial symmetry to complete the occluded areas. Thus, the proposed method can perform comparisons among interpose facial scans, unlike existing methods that require frontal only scans.Σε αυτήν τη διατριβή παρουσιάζεται μία νέα μέθοδος ανίχνευσης 3Δ οροσήμων και υπολογισμού πόζας, κατάλληλη για 3Δ μετωπικές και πλάγιες σαρώσεις προσώπου. Η προτεινόμενη μέθοδος εκμεταλλεύεται την 3Δ και 2Δ πληροϕορία, ώστε με τη χρήση τοπικών περιγραϕέων να εξαχθούν υποψήϕια σημεία ενδιαϕέροντος, που στη συνέχεια θα αναγνωρισθούν και θα ονοματισθούν ως ανατομικά ορόσημα. Επιπλέον, εισάγεται ένα νέο γενικό πλαίσιο συνδυασμού περιγραϕέων χαρακτηριστικών, για την ανίχνευση οροσήμων. Ωστόσο, οι γενικές μέθοδοι ανίχνευσης χαρακτηριστικών, δεν μπορούν να αναγνωρίσουν και να ονοματίσουν τα ανιχνευμένα ορόσημα. Για το σκοπό αυτό, εισάγεται ένα 3Δ Μοντέλο Οροσήμων Προσώπου (Facial Landmark Model - FLM) αποτελούμενο από ανατομικά ορόσημα. Τα υποψήϕια ορόσημα, ανεξάρτητα του τρόπου με τον οποίο έχουν παραχθεί, μπορούν να αναγνωρισθούν και να ονοματισθούν από το ταίριασμά τους με το μοντέλο FLM. Τέλος, εισάγεται μία νέα μέθοδος για την απρόσκοπτη αναγνώριση προσώπου η οποία χρησιμοποιεί τον 3Δ ανιχνευτή οροσήμων για να αποδόσει σε κάθε σάρωση προσώπου μία αρχική εκτίμηση της πόζας και στη συνέχεια, ένα 3Δ Προσημειωμένο Μοντέλο Προσώπου (Annotated Face Model - AFM) ευθυγραμμίζεται και προσαρμόζεται στα δεδομένα, χρησιμοποιώντας τη συμμετρία του προσώπου για τη συμπλήρωση των αποκρυπτόμενων περιοχών. Έτσι, η προτεινόμενη μέθοδος μπορεί να επιτελέσει συγκρίσεις μεταξύ σαρώσεων διαϕορετικής πόζας, σε αντίθεση με τις υπάρχουσες μεθόδους που απαιτούν αποκλειστικά μετωπικές σαρώσεις

Light-wave control of correlated materials using quantum magnetism during time-periodic modulation of coherent transport

Light–wave quantum electronics utilizes the oscillating carrier wave to control electronic properties with intense laser pulses. Without direct light–spin interactions, however, magnetic properties can only be indirectly affected by the light electric field, mostly at later times. A grand challenge is how to establish a universal principle for quantum control of charge and spin fluctuations, which can allow for faster-than-THz clock rates. Using quantum kinetic equations for the density matrix describing non–equilibrium states of Hubbard quasiparticles, here we show that time–periodic modulation of electronic hopping during few cycles of carrier–wave oscillations can dynamically steer an antiferromagnetic insulating state into a metalic state with transient magnetization. While nonlinearities associated with quasi-stationary Floquet states have been achieved before, magneto–electronics based on quasiparticle acceleration by time–periodic multi–cycle fields and quantum femtosecond/attosecond magnetism via strongly–coupled charge–spin quantum excitations represents an alternative way of controlling magnetic moments in sync with quantum transport.</p