Abstract-Rendering a face recognition system robust is vital in order to safeguard it against spoof attacks carried out by using printed pictures of a victim (also known as print attack) or a replayed video of the person (replay attack). A key property in distinguishing a live, valid access from printed media or replayed videos is by exploiting the information dynamics of the video content, such as blinking eyes, moving lips, and facial dynamics. We advance the state of the art in facial anti-spoofing by applying a recently developed algorithm called Dynamic Mode Decomposition (DMD) as a general-purpose, entirely data-driven approach to capture the above liveness cues. We propose a classification pipeline consisting of DMD, Local Binary Patterns (LBP), and Support Vector Machines (SVM) with a histogram intersection kernel. A unique property of DMD is its ability to conveniently represent the temporal information of the entire video as a single image with the same dimensions as those images contained in the video. The pipeline of DMD+LBP+SVM proves to be efficient, convenient to use, and effective. In fact only the spatial configuration for LBP needs to be tuned. The effectiveness of the methodology was demonstrated using three publicly available databases: print-attack, replay-attack, and CASIA-FASD, attaining comparable results with the state of the art, following the respective published experimental protocols.
The selection and application of biometrics traits for authentication and identification have recently attracted a significant amount of research interest. In this paper we investigate the use of keystroke data to distinguish between humans using keystroke biometric systems and non-humans for auditing application. Recently, Benford's Law and Zipf's Law, which are both discrete Power law probability distributions, have been effectively used to detect fraud and discriminate between genuine data and fake/tampered data.As such, our motivation is to apply the Benford's Law and Zipf's Law on keystroke data and to determine whether they follow these laws and discriminate between humans using keystroke biometric systems from non-humans. From the results, we observe that, the latency values of the keystroke data from humans actually follow the Benford's law and Zipf's law, but not the duration values. This implies that, latency values from humans would follow the two laws, whereas the latency values from non-humans would deviate from the Benford's law and Zipf's law.Even though, the duration values from humans deviates from the Benford's law, they do follow a pattern that we can develop an accurate model for the duration values. We perform experiments using the benchmark data set developed by Killourhy and Maxion, CMU [1] and obtain divergences of 0.0008, 0.029 and 0.05 for the keyup-keydown (latency), keydown-keydown, and duration of the keystroke data, respectively. Moreover, P-value's of 0.7770, 0.6230 and 0.0160 are obtained for the keyup-keydown (latency), keydown-keydown, and duration of the keystroke data, respectively.We observe that the latency (which is the time elapsed between release of the first key and pressing down of the next key) is one of the most important features used by administrators for auditing purposes to detect anomalies during their employees logging into their company system.
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The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Naïve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Naïve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life.
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