Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.
Increasing volume of crimes has brought a serious problem to many countries across the world. Crime prevention is an important component of an overall strategy to reduce crime and to strengthen public safety. Although, Supporting Decision Making (SDM) in crime prevention is an important topic but a comprehensive literature review on the subject has yet to be implemented. Thus, this study presents a systematic and comprehensive review on a classification framework for SDM in crime prevention. Forty four journal articles on the subject published between 2000 and October, 2015 were analyzed and classified into two categories of index crime (violent crime and property crime) and six classes of data mining techniques (prediction, classification, visualization, regression, clustering and outlier detection). The results of this study clearly show that data mining especially prediction and clustering techniques have been applied most extensively in both index crime categories. The main data mining techniques used for SDM in crime prevention are Bayesian, neural network and nearest neighbor. This study also addresses the gaps between SDM in crime prevention and the needs of practitioners to encourage more researches in crime analysis. Finally, it concludes with some suggestions for future research on SDM in crime prevention.
Recently, the major challenge faced by all intelligencegathering and law-enforcement organizations is to efficiently and accurately analyze the huge volumes of crime data. Self-organizing map (SOM) can be used to visualize crime data into more comprehensible presentation. Although visualization by using SOM has been done by many researchers, a literature review on the subject focuses on crime data has yet to be implemented. Thus, this study reviews researches on visualization of crime data using SOM and improvement in SOM (Fuzzy SOM or FSOM) together with their available software tools. Information and related works on visualization using SOM and FSOM were studied to fully understand the situation. The result of this study clearly shows that SOM and FSOM have been applied to visualize data in various fields of study. However, there is only limited use of them for visualization of crime data by the researchers. The popular software tool for visualization of crime data using SOM is Matlab, followed by Viscovery SOMine, R, GeoVISTA Studio and C/C++ compiler. Finally, this study concludes with a few suggestions for future researches on visualization of crime data.
Face classification is a challenging task that is crucial to numerous applications. There are many algorithms for classifying gender, but their ability to evaluate their effectiveness regarding scientific data is constrained. Deep learning is popular among researchers in face classification problems. The detection of many faces is complicated and becomes a necessity in real problems. The proposed research aims to examine the effect of twofold face detection approach on the accuracy of gender classification, as well as the effect of using small datasets on accuracy. In this study, we use a small dataset to classify facial images based on their gender. The following phases involve deep learning methods along with the OpenCV library version 3.4.2 which is recommended to serve as a twofold face detection approach. In the experiments conducted, Phase 1 is the designated training phase, and Phase 2 serves as a testing phase. Two different algorithms are used in the testing phase to detect one face in the image (Experiment 1), while the remaining algorithm detects multiple faces in the image (Experiment 2). The FEI dataset is used to evaluate the accuracy of the proposed research, which results in 84% accuracy for Experiment 2 and 74% for Experiment 1, respectively.
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