Optimization of deep learning features for age-invariant face recognition

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods

Age Invariant Face Recognition using Convolutional Neural Network

In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition.  Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Face recognition technology has been used in many ways, such as in the authentication and identification process. The object raised is a piece of face image that does not have complete facial information (occluded face), it can be due to acquisition from a different point of view or shooting a face from a different angle. This object was raised because the object can affect the detection and identification performance of the face image as a whole. Deep leaning method can be used to solve face recognition problems. In previous research, more focused on face detection and recognition based on resolution, and detection of face. Mask region convolutional neural network (mask R-CNN) method still has deficiency in the segmentation section which results in a decrease in the accuracy of face identification with incomplete face information objects. The segmentation used in mask R-CNN is fully convolutional network (FCN). In this research, exploration and modification of many FCN parameters will be carried out using the CNN backbone pooling layer, and modification of mask R-CNN for face identification, besides that, modifications will be made to the bounding box regressor. it is expected that the modification results can provide the best recommendations based on accuracy

Αναγνώριση Προσώπου Ανεξαρτήτως Ηλικίας

Στόχος της παρούσας Πτυχιακής Εργασίας είναι να αναλυθεί το πρόβλημα της αναγνώρισης προσώπου ανεξαρτήτως ηλικίας και τελικά να υλοποιηθεί ένα σύστημα μηχανικής μάθησης για την επίλυση του. Αρχικά παρουσιάζεται μία ποικιλία παλαιότερων τεχνικών που έχουν εφαρμοστεί για το συγκεκριμένο πρόβλημα αλλά μεγαλύτερη έμφαση δίνεται σε προσεγγίσεις μηχανικής μάθησης. Στη συνέχεια, αφού εξεταστεί η διαδικασία της γήρανσης και το πως αυτή όπως και κάποιοι ακόμα σημαντικοί παράγοντες επιδρούν αρνητικά στα συστήματα αναγνώρισης προσώπου, περιγράφονται ορισμένες μέθοδοι μηχανικής μάθησης που έχουν καταφέρει να αντιμετωπίσουν τους παράγοντες αυτούς σε ικανοποιητικό βαθμό. Επίσης αναλύεται λεπτομερώς η δομή που ακολουθούν τα περισσότερα συστήματα αναγνώρισης προσώπου και πιο συγκεκριμένα η δομή των συνελικτικών δικτύων (CNN), όπως και κάποια από τα στοιχεία που καθιστούν τη συγκεκριμένη τεχνική τόσο ισχυρή. Για τους σκοπούς της εργασίας υλοποιήθηκε ένα σύστημα ταυτοποίησης προσώπου και ένα σύστημα επαλήθευσης προσώπου τα οποία αξιολογήθηκαν σε γνωστά σύνολα δεδομένων που χρησιμοποιούνται κατά κύριο λόγο στην αναγνώριση προσώπου ανεξαρτήτως ηλικίας. Μετά την παρουσίαση των συνόλων δεδομένων FG-NET και CACD-VS που χρησιμοποιήθηκαν, γίνεται περιγραφή της υλοποίησης, η οποία περιλαμβάνει όλες τις λεπτομέρειες της αρχιτεκτονικής του συστήματος που κατασκευάστηκε και τα ακριβή βήματα που ακολουθήθηκαν. Τέλος αναφέρονται τα αποτελέσματα του συστήματος στα σύνολα δεδομένων που χρησιμοποιήθηκαν όπως και τα τελικά συμπεράσματα που προέκυψαν από το σύνολο της εργασίας.The purpose of this thesis is to analyze the problem of age invariant face recognition and implement a machine learning system for its solution. First, we introduce a variety of older techniques that have been applied to this subject but we mainly focus on deep learning methods. After considering the whole aging process and some other important factors that affect the face recognition systems, we present some machine learning methods which have achieved coping with these factors. Subsequently we analyze in detail the structure that most of the face recognition systems follow focusing on the architecture of Convolutional Neural Networks, and on the features that make CNN based methods so powerful. For the purpose of this thesis we implement two systems for face identification and face verification respectively and evaluate them on popular datasets which have been mostly used for age invariant face recognition. After the presentation of FG-NET and CACD-VS datasets we describe the overall process of implementation mentioning the exact steps we followed and explaining every detail of the system architecture. Finally, we report the results that derived from our implementation based on the two datasets