Non-negative locality-constrained vocabulary tree for finger vein image retrieval

Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency. However, there is a large accumulative quantization error in the vocabulary tree (VT) model that may degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performanee and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performanee than other state-of-theart methods, while maintaining low time complexity.

Multi-task MIML learning for pre-course student performance prediction

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning activities.However,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual students.In this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course starting.However,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the end.In this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its commencement.To better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)problem.Besides,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified framework.Extensive experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

A hybrid biometric identification framework for high security applications

Research on biometrics for high security applica- tions has not attracted as much attention as civilian or foren- sic applications. Limited research and deficient analysis so far has led to a lack of general solutions and leaves this as a challenging issue. This work provides a systematic analy- sis and identification of the problems to be solved in order to meet the performance requirements for high security applica- tions, a double low problem. A hybrid ensemble framework is proposed to solve this problem. Setting an adequately high threshold for each matcher can guarantee a zero false accep- tance rate （FAR） and then use the hybrid ensemble framework makes the false reject rate （FRR） as low as possible. Three ex- periments are performed to verify the effectiveness and gener- alization of the framework. First, two fingerprint verification algorithms are fused. In this test only 10.55% of fingerprints are falsely rejected with zero false acceptance rate, this is sig- nificantly lower than other state of the art methods. Second, in face verification, the framework also results in a large re- duction in incorrect classification. Finally, assessing the per- formance of the framework on a combination of face and gait verification using a heterogeneous database show this frame- work can achieve both 0% false rejection and 0% false accep- tance simultaneously.