Combining multiple biometrics to protect privacy

As biometrics are gaining popularity, there is increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. The association of fingerprints with criminals raises further concerns. On the other hand, the alternative suggestion of keeping biometric data in smart cards does not solve the problem, since forgers can always claim that their card is broken to avoid biometric verification altogether. We propose a biometric authentication framework which uses two separate biometric features combined to obtain a non-unique identifier of the individual, in order to address privacy concerns. As a particular example, we demonstrate a fingerprint verification system that uses two separate fingerprints of the same individual. A combined biometric ID composed of two fingerprints is stored in the central database and imprints from both fingers are required in the verification process, lowering the risk of misuse and privacy loss. We show that the system is successful in verifying a person’s identity given both fingerprints, while searching the combined fingerprint database using a single fingerprint, is impractical

Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

Biometrics in ABC: counter-spoofing research

Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks

Hybrid Fusion for Biometrics: Combining Score-level and Decision-level Fusion

A general framework of fusion at decision level, which works on ROCs instead of matching scores, is investigated. Under this framework, we further propose a hybrid fusion method, which combines the score-level and decision-level fusions, taking advantage of both fusion modes. The hybrid fusion adaptively tunes itself between the two levels of fusion, and improves the final performance over the original two levels. The proposed hybrid fusion is simple and effective for combining different biometrics

Supervised Hashing for Retrieval of Multimodal Biometric Data

Biometric systems commonly utilize multi-biometric approaches where a person is verified or identified based on multiple biometric traits. However, requiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. These are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing techniques that are available for obtaining retrieval. We specifically based on our analysis recommend the use of supervised hashing techniques over deep learned features as a possible common technique to solve this problem. Our investigation includes a comparison of some of the supervised and unsupervised methods viz. Principal Component Analysis (PCA), Locality Sensitive Hashing (LSH), Locality-sensitive binary codes from shift-invariant kernels (SKLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint for a number of standard datasets. The main technical contributions through this work are as follows: (a) Proposing Siamese network based deep learned feature extraction method (b) Analysis of common feature extraction techniques for multiple biometrics as to a reduced feature space representation (c) Advocating the use of supervised hashing for obtaining a compact feature representation across different biometrics traits. (d) Analysis of the performance of deep representations against shallow representations in a practical reduced feature representation framework. Through experimentation with multiple biometrics traits, feature representations, and hashing techniques, we can conclude that current deep learned features when retrieved using supervised hashing can be a standard pipeline adopted for most unimodal and multimodal biometric identification tasks.</p