Organising a photograph collection based on human appearance

This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

Result Oriented Based Face Recognition using Neural Network with Erosion and Dilation Technique

It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery and wearing the spec/glasses which are the new challenge to automatic face recognition. Face detection is one of the challenging problems in the image processing. This seminar, introduce a face detection and recognition system to detect (finds) faces from database of known people. To detect the face before trying to recognize it saves a lot of work, as only a restricted region of the image is analyzed, opposite to many algorithms which work considering the whole image. In This , we gives study on Face Recognition After Plastic Surgery (FRAPS )and after wearing the spec/glasses with careful analysis of the effects on face appearance and its challenges to face recognition. To address FRAPS and wearing the spec/glasses problem, an ensemble of An Optimize Wait Selection By Genetic Algorithm For Training Artificial Neural Network Based On Image Erosion and Dilution Technology. Furthermore, with our impressive results, we suggest that face detection should be paid more attend to. To address this problem, we also used Edge detection method to detect i/p image properly or effectively. With this Edge Detection also used genetic algorithm to optimize weight using artificial neural network (ANN)and save that ANN file to database .And use that ANN file to compare face recognition in future DOI: 10.17762/ijritcc2321-8169.16041

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure