Computational workload in biometric identification systems: an overview <s> Same feature, different representation: <s> There have been claims of very high information content in iris texture, higher even than in fingerprints. This makes iris attractive for large scale identification systems with possibly millions of people. However, some systems operate by performing N 1:1 matches of the probe against the database. This can get prohibitively expensive in terms of computation as N grows large. Note that for identification systems the per-match time dominants system performance, unlike verification where feature extraction time is the primary component. In this paper we show how to use a short-length iris code to pre-screen a large database and thereby reduce the number of full comparisons needed to a fraction of the total. Since the screening code is much smaller than the full iris code, the time to process the whole database is greatly reduced. As an added benefit, we show that we can use the alignment inferred from the short code to greatly restrict the range of alignments searched for the full code, which further speeds up the system. As we demonstrate in experiments, the two stage approach can reduce the cost and/or time needed by an order of magnitude with very little impact on identification performance. <s> BIB001 </s> Computational workload in biometric identification systems: an overview <s> Same feature, different representation: <s> State-of-the-art image retrieval systems achieve scalability by using bag-of-words representation and textual retrieval methods, but their performance degrades quickly in the face image domain, mainly because they 1) produce visual words with low discriminative power for face images, and 2) ignore the special properties of the faces. The leading features for face recognition can achieve good retrieval performance, but these features are not suitable for inverted indexing as they are high-dimensional and global, thus not scalable in either computational or storage cost. In this paper we aim to build a scalable face image retrieval system. For this purpose, we develop a new scalable face representation using both local and global features. In the indexing stage, we exploit special properties of faces to design new component-based local features, which are subsequently quantized into visual words using a novel identity-based quantization scheme. We also use a very small hamming signature (40 bytes) to encode the discriminative global feature for each face. In the retrieval stage, candidate images are firstly retrieved from the inverted index of visual words. We then use a new multi-reference distance to re-rank the candidate images using the hamming signature. On a one-millon face database, we show that our local features and global hamming signatures are complementary — the inverted index based on local features provides candidate images with good recall, while the multi-reference re-ranking with global hamming signature leads to good precision. As a result, our system is not only scalable but also outperforms the linear scan retrieval system using the state-of-the-art face recognition feature in term of the quality. <s> BIB002 </s> Computational workload in biometric identification systems: an overview <s> Same feature, different representation: <s> Personal identification based on finger vein patterns is a newly developed biometrics technique and several practical systems have been deployed recent years. We developed a finger vein verification system for checking attendance and have collected a database of 0.8 million finger vein samples. Based on the database, we proposed a person retrieval solution for searching an image in the database and can get the response in an acceptable time. To fit for the retrieval solution, we designed a new encoding method. The experimental results show that our solution can get a result in about 10 seconds when working on a database of 50,700 samples. In the same time, the error rate is nearly the same as the linear searching. <s> BIB003 </s> Computational workload in biometric identification systems: an overview <s> Same feature, different representation: <s> Conventional speaker identification systems are already field-proven with respect to recognition accuracy. Since any biometric identification requires exhaustive 1 : N comparisons for identifying a biometric probe, comparison time frequently dominates the overall computational workload, preventing the system from being executed in real-time. In this paper we propose a computational efficient two-stage speaker identification system based on Gaussian Mixture Model and Universal Background Model. Binarized voice biometric templates are utilized to pre-screen a large database and thereby reduce the required amount of full comparisons to a fraction of the total. Experimental evaluations demonstrate that the proposed system is capable of significantly accelerating the response-time of the system and, at the same time, identification performance is maintained, confirming the soundness of the scheme. <s> BIB004 </s> Computational workload in biometric identification systems: an overview <s> Same feature, different representation: <s> Feature extraction techniques such as local binary patterns (LBP) or binarized statistical image features (BSIF) are crucial components in a biometric recognition system. The vast majority of relevant approaches employs spectral histograms as feature representation, i.e. extracted biometric reference data consists of sequences of histograms. Transforming these histogram sequences to a binary representation in an accuracy-preserving manner would offer major advantages w.r.t. data storage and efficient comparison. We propose a generic binarization for spectral histogram models in conjunction with a Hamming distance-based comparator. The proposed binarization and comparison technique enables a compact storage and a fast comparison of biometric features at a negligible cost of biometric performance (accuracy). Further, we investigate a serial combination of the binary comparator and histogram model-based comparator in a biometric identification system. Experiments are carried out for two emerging biometric characteristics, i.e. palmprint and ear, confirming the soundness of the presented technique. <s> BIB005
The key idea behind this category of approaches is transformation of the original feature representation into a more compact one, whereby the computational costs of comparisons are decreased (often at the cost of losing some discriminative power). The compact templates can then be used to reduce the search space (i.e. by acting as an index, which allows to determine a candidate short-list). Conceptually similar approaches, where binarised (see also subsection 3.4.1) and/or shortened feature vectors are used as an index in the pre-filtering step, have been proposed e.g. in BIB002 BIB001 BIB003 BIB004 BIB005 for iris, face, fingervein, voice, and ear, respectively. The difference between the key idea in this and previous subsection is subtle -here, the same feature is used to create the index template (e.g. through binarisation), whereas in the multifeature concept, additional features are extracted from the sample (e.g. through texture or keypoint descriptors or high level geometric features).