Presentation attack detection for face recognition on smartphones: a comprehensive review

Even though the field of Face Presentation Attack Detection (PAD) has been around for quite a long time, but still it is quite a new field to be implemented on smartphones. Implementation on smartphones is different because the limited computing power of the smartphones when compared to computers. Presentation Attack for a face recognition system may happen in various ways, using photograph, video or mask of an authentic user’s face. The Presentation Attack Detection system is vital to counter those kinds of intrusion. Face presentation attack countermeasures are categorized as sensor level or feature level. Face Presentation Attack Detection through the sensor level technique involved in using additional hardware or sensor to protect recognition system from spoofing while feature level techniques are purely software-based algorithms and analysis. Under the feature level techniques, it may be divided into liveness detection; motion analysis; face appearance properties (texture analysis, reflectance); image quality analysis (image distortion); contextual information; challenge response. There are a few types of research have been done for face PAD on smartphones. They also have released the database they used for their testing and performance benchmarking

Food intake gesture monitoring system based-on depth sensor

Food intake gesture technology is one of a new strategy for obesity people managing their health care while saving their time and money. This approach involves combining face and hand joint point for monitoring food intake of a user using Kinect Xbox One camera sensor. Rather than counting calories, scientists at Brigham Young University found dieters who eager to reduce their number of daily bites by 20 to 30 percent lost around two kilograms a month, regardless of what they ate [1]. Research studies showed that most of the methods used to count bite are worn type devices which has high false alarm ratio. Today trend is going toward the non-wearable device. This sensor is used to capture skeletal data of user while eating and train the data to capture the motion and movement while eating. There are specific joint to be capture such as Jaw face point and wrist roll joint. Overall accuracy is around 94%. Basically, this increase in the overall recognition rate of this system

A Study on Human Fall Detection Systems: Daily Activity Classification and Sensing Techniques

Fall detection for elderly is a major topic as far as assistive technologies are concerned. This is due to the high demand for the products and technologies related to fall detection with the ageing population around the globe. This paper gives a review of previous works on human fall detection devices and a preliminary results from a developing depth sensor based device. The three main approaches used in fall detection devices such as wearable based devices, ambient based devices and vision based devices are identified along with the sensors employed.  The frameworks and algorithms applied in each of the approaches and their uniqueness is also illustrated. After studying the performance and the shortcoming of the available systems a future solution using depth sensor is also proposed with preliminary results

Data pre-processing on web server logs for generalized association rules mining algorithm

Web log file analysis began as a way for IT administrators to ensure adequate bandwidth and server capacity on their organizations website. Log file data can offer valuable insight into web site usage.It reflects actual usage in natural working condition, compared to the artificial setting of a usability lab.It represents the activity of many users, over potentially long period of time, compared to a limited number of users for an hour or two each.This paper describes the pre-processing techniques on IIS Web Server Logs ranging from the raw log file until before mining process can be performed. Since the pre-processing is tedious process, it depending on the algorithm and purposes of the applications

Collaboration among teachers in inclusive special education program classrooms

Collaboration between teachers and special education teachers is a vital factor in ensuring the success of the implementation of the Inclusive Education Programs especially in the teaching and learning aspects. Until 2016, there is no detailed document or information regarding how the collaboration is conducted or implemented in the inclusive classrooms. Therefore, this study is aimed to explore how collaboration is conducted and implemented in the Inclusive Special Education Program classrooms, particularly identifying the stage of collaboration the school are at and approach used in the inclusive classrooms. To achieve this, a survey questionnaire was used as an instrument for this study and it is conducted on 53 schools and 441 participants including headmasters, senior assistant principals for special education teachers, subject teachers and special education teacher. Results showed that most of the participants are at the “Starting a partnership” stage (mean score = 4.165, SD = 0.797). The type of collaboration approach that are usually being used is the “collaboration-consultation” approach (mean score = 4.10, SD = 0.721). This suggests that more action is needed to ensure the successful implementation of an inclusive program. This is because without done the recommended steps of collaboration, the inclusive may not carried out effectively. A successful inclusive classroom also can be achieved through multiple approaches of collaboration are implemented. Therefore, knowledge about steps and approaches of collaboration should be knowledgeable to general, special education teacher and also school administrators

Development of Human Fall Detection System using Joint Height, Joint Velocity, and Joint Position from Depth Maps

Human falls are a major health concern in many communities in today’s aging population. There are different approaches used in developing fall detection system such as some sort of wearable, ambient sensor and vision based systems. This paper proposes a vision based human fall detection system using Kinect for Windows. The generated depth stream from the sensor is used in the proposed algorithm to differentiate human fall from other activities based on human Joint height, joint velocity and joint positions. From the experimental results our system was able to achieve an average accuracy of 96.55% with a sensitivity of 100% and specificity of 95