Gabriel Costache

In-camera person-indexing of digital images

A prototype implementation of an automatic in-camera cataloging tool is presented. An external infrastructure to store and analyze images and support the in-camera cataloging tool is also described.

Automatic indexing of consumer image collections using person recognition techniques

An evaluation of person recognition technologies for use in an automatic cataloging tool for consumer image collections is presented. Prototype implementations of the automatic cataloging tool and user search tools are described. A preliminary statistical analysis of the success rate of these tools is given.

Automated sorting of consumer image collections using face and peripheral region image classifiers

An evaluation of techniques and recognition technologies for use in an automatic cataloging tool for consumer image collections is presented. A working implementation of an automatic cataloging tool and user search tools are described. Techniques for combining multiple classifiers derived from face and peripheral regions are discussed. Practical aspects of the requirements for a working in-camera solution are also discussed.

Combining PCA-based datasets without retraining of the basis vector set

A method of combining multiple PCA datasets together with re-projecting the dataset into the new PCA space is presented which does not require preservation of the original datasets from which the PCA descriptors were derived. Practical applications based on face recognition are described where (i) multiple PCA datasets can be combined and (ii) an existing PCA dataset can be augmented with a new set of original data samples. Test results performed on a database of 560 facial regions indicate that this method yields practically identical results with the classical approach of retraining over the original dataset.

A Review of Face Recognition Techniques for In-Camera Applications

This paper presents an overview of a number of classical face recognition techniques, including an assessment of their suitability for applications in real-time embedded systems such as digital cameras, and a generalized discussion of the training requirements for different face recognition techniques. Also, an introduction to face recognition approaches based on hidden Markov models (HMM) is made, and their advantages and benefits are shown, particularly their ability to provide acceptable levels of accurate recognition for unseen collections of facial images. Finally, a detailed description/implementation of a working HMM test bed is done, together with a presentation of some preliminary results.

Real-Time Video Face Recognition for Embedded Devices

This chapter will address the challenges of real-time video face recognition systems implemented in embedded devices. Topics to be covered include: the importance and challenges of video face recognition in real life scenarios, describing a general architecture of a generic video face recognition system and a working solution suitable for recognizing faces in real-time using low complexity devices. Each component of the system will be described together with the system’s performance on a database of video samples that resembles real life conditions.

Picture Management using Person Retrieval for Consumer Image Collections

In the last years the huge evolution of digital photography lead to an increasing interest in developing algorithms for indexing and classifying collections of digital images. This paper presents an automatic system for organizing and browsing through consumer digital image collections using the persons in the images as patterns. In order to implement such an automatic system we have to detect and classify the people in the images according to their similarities. For this we employ algorithms for face detection, face recognition and additional methods to cope with large variations that are usually present in consumer images. These additional methods includes using more than one type of classifiers for face recognition and also using additional information about the person characteristics extracted from other region than the face. This additional information will be more robust to factors that influence the accuracy of classical face recognition systems when working with consumer images. The proposed system was tested using a typical consumer image collection and practical applications using the system are presented in the end.