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Spotlight In this spotlight we would like to point out the relationship between two cool research topics, namely biometric recognition and deep learning, and between them and industry latest trends. It is quite usual that successful research products reach the industry application after that consolidated results testified their effectiveness. Biometrics and deep learning are not an exception. In fact, leaving out fingerprint recognition that has a long story of application in both forensics and security, the other traits have a much more recent presence in real-world applications. The first mention of face recognition out of the fields of experimental psychology and cognitive neurosciences dates back to ?60, but entails a semi-automatic (forensic) process supported by human experts. In 1988, Kirby and Sirovich applied principle component analysis to the face recognition problem and following this work, in 1991, Turk and Pentland proposed Eigenfaces. However, the approach was successful with a small dataset and in extremely controlled conditions. In order to have the first commercial off-the-shelf (COTS) face recognition systems, though still limited to well controlled scenarios, we have to wait until 1997. Joseph Atick, a pioneer of facial recognition, helped developing the first facial recognition system in the world and founded the American company Visionics, one of the first companies in the field, in 1994. In August 1997, Visionics announced the first large scale shipping of its FaceIt(R) face recognition software engine to application developers and system integrators. The engine was encapsulated into a powerful Software Developer Kit to enable developers to quickly incorporate facial recognition capabilities into their applications, even beyond the forensics context. Afterwards, in November 1998, Visionics Corporation announced that its FaceIt(R) face recognition software was the first software of its kind to be used in a Closed Circuit Television (CCTV) control room application. The new CCTV-based anti-crime system called 'Mandrake' was installed in the borough of Newham, East London, in the United Kingdom. In March 2000 two early direct competitors, Viisage Technology, Inc. and Biometrica Systems, Inc. announced the fiftieth order and installation of its full surveillance suite of Visual Casino loss-reduction systems, at Mirage Resort, Las Vegas. Visual Casino, a computer application with its facial recognition technology, was used by surveillance operators to identify cheaters and other casino undesirables, as well as casino VIPs. After that new competitors entered the market, being Cognitec one of e most renowned nowadays. Another biometric trait that has reached the market is iris, with a similar history from the first significant work presented by J. Daugman in 1993, except for the fact that it often requires special equipment. . In 1995, the first commercial product was distributed on the market by Iriscan, Inc., that become Iridian Technologies, Inc. in 2000 (that in 2006 was acquired by Viisage Technologies, Inc.). In 2005, the broad patent covering iris recognition expired, so that other companies could develop their own algorithms for iris recognition. The patent on the IrisCodes® has expired too in 2011. Going to deep learning, it gave a second life to neural networks, and is gaining increasing attention in industry too. Notwithstanding its definition as a ?a new area of Machine Learning research? (http://deeplearning.net) many researchers consider the Neocognitron introduced by Kunihiko Fukushima in 1980 as one of the first ancestors of deep learning architectures, in particular of those deriving from artificial neural networks (ANN). However, it is only after 2007 that the work by Yoshua Bengio renewed the research community interest on related topics. ?The large-scale impact of Deep Learning in Big Tech Companies began around 2010 with speech recognition. ? It took around 30 years to become mainstream. Computers were not powerful enough and companies didn?t have such a large amount of data.? (http://tinyurl.com/lnv77uo). At present, Skymind offers to enterprise deep learning for big data, e.g., company fraud detection in finance, recommender systems in e-commerce, and anomaly detection for hardware makers. But, as noticed in December 2014 by Forbes (http://tinyurl.com/kwswv7p) big American companies like Google, IBM, Microsoft, Facebook and China?s Baidu are well-represented in applications providing the core technologies for applications addressing a radical change in enterprise and industry. Last but not least, the use of deep learning as a strategy for biometric extends from research to industry and to social networks. DeepIris by CASIA (Chinese Academy of Science) is one of the latest proposals for iris recognition. But in the ?real world? Google already uses deep learning for voice recognition on Android phones, and Facebook uses DeepFace to identify friends in users?photographs. To this aim, the latter exploits ?a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities.? (http://tinyurl.com/kco8e7q) What is the key for this successful marriage? According to many observers, the availability of big amounts of data is a possible explanation of recent achievements. From one side, it might represent a possible stressing factor for machine learning methods. However, the increased computing power and storage easily compensate this. On the other side, the main the positive aspect of this wealth of data is the possibility to carry out a huge training and a fine-tuning of a large number parameters relevant to classification, that was impossible before.
Paper Summary Overview: Cardiovascular Biom et rics: Com bining Mechanical and Elect rical Signals (* ) Ikenna Odinaka a , Joseph A. O'Sullivan b , Erik J. Sirevaag c , John W. Rohrbaugh c ikenna.odinaka@duke.edu a - Duke University, b- Washington University in Saint Louis c - Washington University School of Medicine Two biometric traits that have been investigated for their potential use in human recognition are the electrocardiogram (ECG) and the laser Doppler vibrometry (LDV) signal. Although the ECG and LDV signals are both measures of cardiovascular activity, the differences between them are substantial and important, and reflect fundamentally different aspects of cardiovascular performance. Whereas the ECG is an electrical signal, which is generated in the myocardium and is conducted by volume to the surface electrodes, the LDV signal (recorded here from the neck, overlying the carotid artery) derives from mechanical displacements of the skin overlying the carotid artery. Previous studies have shown that biometric systems based on the ECG or LDV signal can achieve single digit authentication performance, in terms of equal error rate (EER). In particular, EERs between 5 and 7% have been reported, when training and testing data are obtained from different sessions [1], [2]. In this work, we propose combining ECG and LDV signals for human recognition. In addition, we employ standard fusion techniques for aggregating the information provided by each biometric system, to achieve a substantial increase in recognition performance. We show that a reduction by at least half in the EER can be obtained from this fusion. Moreover, we report on the methods we have found to work best for authentication and identification. The principal goal of this effort is thus to assess authentication and identification performance using combined ECG and LDV signals. We pursued this objective using a number of well-established and accepted fusion methods. A multibiometric system is one that uses more than one biometric trait (multimodal), biometric classifier (multi-algorithm), instance of the same biometric trait (multi-instance), sensor for extracting the same biometric trait (multi-sensor), or sample of the same biometric trait (multi-sample) for biometric recognition [3]. A multi-instance system applies to biometric traits that do not occur singly; e.g., ten fingers, two irises, and multiple ECG channels. A multi-sample system combines multiple samples or different portions of the same biometric trait; e.g. the front and side of a face, segments of the same finger, multiple ECG heartbeats. For physiologically related signals, which change over time, we can combine signals from more than one session, the so called multi-session system [1]. In this work, we present the performance gains that result from a multimodal, multi-session, multi-sample system involving the ECG and LDV signals. The individual systems that constitute a multibiometric system can be combined at multiple levels, ranging from the raw signal to decision processes [4]. In this work, we compare the authentication and identification performance of the cardiovascular multibiometric system, when fusion is performed at the feature, score, rank, and decision levels of the biometric pipeline. In addition, we analyze the performance gains of the cardiovascular multibiometric system over the individual ECG and LDV biometric systems. The subtle differences between ECG and LDV signals may not be readily appreciated, and it may not be directly obvious that combining the ECG and LDV signals will lead to improved performance. For example, based on Figure 1, the use of temporal fiducial features may decrease the performance of the joint biometric system, since the timing information of the two signals are highly correlated. However, the waveform features of the two signals are very different, suggesting that amplitude-based, angle-based, area-based fiducial features, and non-fiducial features are better able to capture uncorrelated information from the ECG and LDV signals. We adopted the short-time Fourier transform approach to feature extraction. This is by no means the only way the features can be gotten from each modality for later fusion. See the paper by Odinaka et al. [2] for a review of the feature extraction methods that have been proposed for ECG biometrics. Informat ion Fusion: 1) Feature-level Fusion: We concatenate the features vectors from the different modalities to obtain a fused vector. A classifier is then employed on the fused vectors. 2) Score-level Fusion: We explored the three major approaches to score-level fusion: score normalization, density estimation, and classifier-based fusion [3], [4]. Examples of score normalization techniques include: Min-Max, Z-Score (Mean-Std), Median-MAD (median absolute deviation). (* ) Appeared in IEEE Transactions on Information Forensics and Security, 10(1):16-27. January, 2015.
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