With increasing online fraud and identity theft each day, service providers need a way to ensure that their services cannot be compromised. Anti-spoofing liveness detection is required especially in unsupervised authentication situations. Biometric authentication systems need to prevent sophisticated spoofing challenges from replay attacks and determine the user's presence. Thus, BioID's presentation attack detection (PAD) is crucial for eKYC onboarding, online login and banking transactions. BioID is a pioneer and the leading player in face liveness detection for assured user presence.
Face detection is one of the most common applications of Artificial Intelligence. From camera applications in smartphones to Facebook's tag suggestions, the use of face detection in applications is increasing every single day. Face detection is the ability of a computer program to identify and locate human faces in a digital image. With the increasing demand for face detection feature in applications, everyone is looking to use face detection in their application so that they are not left behind in the race. In this post, I will teach you how to build a face detection program for yourself in less than 3 minutes.
Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where unpredictability of new terms requires adaptation in the vector space model. We present a novel event detection system based on the Incremental Term Frequency-Inverse Document Frequency (TF-IDF) weighting incorporated with Locality Sensitive Hashing (LSH). Our system could efficiently and effectively adapt to the changes within the data streams of any new terms with continual updates to the vector space model. Regarding miss probability, our proposed novelty detection framework outperforms a recognised baseline system by approximately 16% when evaluating a benchmark dataset from Google News.
One of the primary issues with traditional anomaly detection approaches is their inability to handle complex, structural data. One approach to this issue involves the detection of anomalies in data that is represented as a graph. The advantage of graph-based anomaly detection is that the relationships between elements can be analyzed, as opposed to just the data values themselves, for structural oddities in what could be a complex, rich set of information. However, until now, attempts at applying graph-based approaches to anomaly detection have encountered two issues: (1) Numeric values found in the data are not incorporated into the analysis of the structure, which could augment and improve the discovery of anomalies; and (2) The anomalous substructure may not be a deviation of the most prevalent pattern, but deviates from only one of many normative patterns. This paper presents enhancements to existing graph-based anomaly detection techniques that address these two issues and shows experimental results validating the usefulness of these enhancements.
In today's competitive market, digital businesses such as fintech, ad tech, media and others are always on the lookout for the next big thing to help streamline their business processes. These businesses are constantly generating new data and often have systems and people in place to monitor what is going on. For example, within one company, you might find an IT group monitoring network performance while someone in product management watching page response time and user experience while marketing analysts track conversions per campaign and other KPIs. It is no secret that anomalies in one area often affect performance in other areas, but it is difficult for the association to be made if all the departments are operating independently of one another. In addition, most of the available tools for this type of monitoring look at what has happened in the past, so there is a built-in delay between when something important happens, and when it may (or may not) be discovered via the monitoring process.