Hyeonjoon Moon

The FERET evaluation methodology for face-recognition algorithms

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

Computational and performance aspects of PCA-based face-recognition algorithms

Algorithms based on principal component analysis (PCA) form the basis of numerous studies in the psychological and algorithmic face-recognition literature. PCA is a statistical technique and its incorporation into a face-recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions, including those not documented in the literature. We experimented with different implementations of each module, and evaluated the different implementations using the September 1996 FERET evaluation protocol (the de facto standard for evaluating face-recognition algorithms). We experimented with (i) changing the illumination normalization procedure; (ii) studying effects on algorithm performance of compressing images with JPEG and wavelet compression algorithms; (iii) varying the number of eigenvectors in the representation; and (iv) changing the similarity measure in the classification process. We performed two experiments. In the first experiment, we obtained performance results on the standard September 1996 FERET large-gallery image sets. In the second experiment, we examined the variability in algorithm performance on different sets of facial images. The study was performed on 100 randomly generated image sets (galleries) of the same size. Our two most significant results are (i) changing the similarity measure produced the greatest change in performance, and (ii) that difference in performance of ±10% is needed to distinguish between algorithms.

The FERET verification testing protocol for face recognition algorithms

Two critical performance characterizations of biometric algorithms, including face recognition, are identification and verification. Identification performance of face recognition algorithms on the FERET tests has been previously reported. We report on verification performance obtained from the Sept96 FERET test. The databases used to develop and test algorithms are usually smaller than the databases that will be encountered in applications. We examine the effects of size of the database on performance for both identification and verification.

The FERET Evaluation

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. The FERET database is divided into two portions. The development portion is provided to researchers for algorithm development and the sequestered portion provides a set of images not seen by the researchers to test algorithms. The set of test is the third in a sequence of FERET tests. This test was administered in September 1996 and March 1997. The Sept96 test provided a detailed assesment of the state of the art, measurement of algorithm performance on large databases, and a comparison among face recognition algorithms.

A verification protocol and statistical performance analysis for face recognition algorithms

Two key performance characterization of biometric algorithms (face recognition in particular) are (1) verification performance and (2) and performance as a function of database size and composition. This characterization is required for developing robust face recognition algorithms and for successfully transitioning algorithms from the laboratory to real world. In this paper we (1) present a general verification protocol and apply it to the results from the Sep96 FERET test, and (2) discuss and present results on the effects of database size and variability on identification and verification performance.

The FERET September 1996 Database and Evaluation Procedure

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. In this paper, we report on the FERET database and the September 1996 FERET test. This test is the third in a series of supervised face-recognition test administered under the FERET program.

Object Detection Using FAST Corner Detector Based on Smartphone Platforms

in this paper, we proposed a real-time object recognition system under smart phone environments. The proposed object recognition system consists of two key modules: feature extraction and object recognition. Feature detectors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Compared to PC platforms, smart phone platforms have limited resources, so computation-intensive SIFT and SURF descriptors are less usable in such resource-limited environments. In this paper utilizes the FAST corner detector that provides faster feature computation by extracting only corner information. The number of corners detected by the FAST corner detector varies so normalization is applied to adjust the extracted corners (interest points) to the same number. Based on the normalized corner information, support vector machine (SVM) and back-propagation neural network (BPNN) training are performed for the efficient recognition of objects. Compared to conventional SIFT and SURF algorithms, the proposed object recognition system based on the FAST corner detector yields increased speed and low performance degradation on smart phones.

Biometrics Person Authentication Using Projection-Based Face Recognition System in Verification Scenario

There are tremendous need for personal verification and identification in internet security, electronic commerce and access control in recent years. Also, as the demands for security in many applications such as data protection and financial transaction become an increasingly relevant issues, the importance of biometrics technology is rapidly increasing. We explored face recognition system for person authentication applications by explicitly state the design decisions by introducing a generic modular PCA face recognition system. We designed implementations of each module, and evaluate the performance variations based on virtual galleries and probe sets. We perform various experiments and report results using equal error rates (EER) for verification scenario. In our experiment, we report performance results on 100 randomly generated image sets (galleries) of the same size.

Biometric Authentication for Border Control Applications

We propose an authentication methodology that combines multimodal biometrics and cryptographic mechanisms for border control applications. We accommodate faces and fingerprints without a mandatory requirement of (tamper-resistant) smart-card-level devices on e-passports for easier deployment. It is even allowable to imprint (publicly readable) bar codes on the passports. Additionally, we present a solution based on the certification and key management method to control the validity of passports within the current Public-Key Infrastructure (PKI) technology paradigm.

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes.