This article proposes a feature extraction method for automatic person recognition in video. The method proposed by Viola and Jones (Viola, P. and Jones, M., 2001 Viola, P. and Jones, M. Rapid object detection using a boosted cascade of simple features. IEEE international conference on computer vision and pattern recognition (CVPR 2001). Kauai Marriott, Hawaii. Vol. 1, pp.511–518. [Google Scholar]. Rapid object detection using a boosted cascade of simple features. In: IEEE international conference on computer vision and pattern recognition (CVPR 2001), Kauai Marriott, Hawaii, Vol. 1, 511–518) is used to detect the face region. Face region is processed in YC b C r colour space to determine the locations of the eyes. The centre of the mouth is determined relative to the locations of the eyes. Facial and mouth features are extracted using multiscale morphological erosion and dilation operations, respectively. The facial features are extracted relative to the locations of the eyes, and mouth features are extracted relative to the locations of the eyes and mouth. Fovea intensity comparison code and exclusive-OR operation for matching are used to recognise a person in video sequences. Projected fovea intensity comparison code (PFICC) and Euclidean distance for matching are also used to recognise a person in video sequences. The performance of the system using PFICC is evaluated in real time in the laboratory environment, and the system achieves a recognition rate (RR) of 99.0% and an equal error rate (EER) of about 0.84% for 50 subjects. The performance of the system is also evaluated for the eXtended Multi Modal Verification for Teleservices and Security (XM2VTS) database, and the system achieves an RR of 100% and an EER of about 0.23%.

Learning to program is a unique experience for each student, and it is not fully understood why one person in an introductory programming course learns to program better and more quickly than the next. Self-efficacy is an individual's judgment of his or her ability to perform a task within a specific domain [1]. A mental model is a person's internal (mental) representation of real world objects and systems [9]. Research has shown that high self-efficacy and a good mental model are important to knowledge acquisition and transfer. This research investigates the effects of students' self-efficacy and mental models of programming on learning to program. The results show that self-efficacy for programming is influenced by previous programming experience and increases as a student progresses through an introductory programming course. The results also show that the student's mental model of programming influences self-efficacy and that both the mental model and self-efficacy affect course performance.

This paper reports on two experiments comparing mental representations and program comprehension by novices in the object-oriented and procedural styles. The subjects were novice programmers enrolled in a second course in programming which taught either the object-oriented or the procedural paradigm. The first experiment compared the mental representations and comprehension of short programs written in the procedural and object-oriented styles. The second experiment extended the study to a larger program incorporating more advanced language features. For the short programs there was no significant difference between the two groups with respect to the total number of questions answered correctly, but the object-oriented subjects were superior to the procedural subjects at answering questions about program function. This suggests that function information was more readily available in their mental representations of the programs and supports an argument that the object-oriented notation highlights function at the level of the individual class. For the long program a corresponding effect was not found. The comprehension of procedural subjects was superior to object-oriented subjects on all types of question. The difficulties experienced by the object-oriented subjects in answering questions in a larger program suggest that they faced problems in marshaling information and drawing inferences from it. We suggest that this result may be related to a longer learning curve for novices of the object-oriented style, as well as to features of the OO style and the particular OO language notation.