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Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results.Key Words: Biometrics, Cancelable Biometrics, Security, Biometric Salting, Non-Invertible Transformation INTRODUCTIONThe biometric traits possessed by each individual are unique and has the potential to recognize an individual. Biometric traits can be physical and behavioral. Therefore, biometrics are used for authentication or recognition of individuals for many critical applications like border control, access control, immigration, forensic and different law enforcement. There are two phases in every conventional biometric system: enrolment phase and authentication phase. In the enrolment stage, the original biometric trait is captured and saved in the database. During the authentication stage, the system matches that stored template every time the user access the system by providing the live biometric (Kaur et al. 2014).Compared to password or token based authentication system, biometric system using fingerprint, iris, face, voice, etc. provides better security as people cannot lose or forget their biometric trait. But, the advanced technology of today's world makes it possible to create a loophole in the biometric system. People leave their fingerprints on whatever they touch; hence one can easily steal the fingerprint and can even make an artificial finger using the stolen fingerprint. The person's face can be captured by the camera even from a distance without their concern. In such situation, the security of the biometric based authentication system is at a stake.To overcome the problem of the stolen biometrics, the researchers have developed the template protection schemes. The biometric template protection schemes are mainly divided into two categories: i) Biomet...

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Abstract-Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%-20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.

The increased in number and size of larvae and juvenile growth are estimated based on manual approach in fishing hatcheries. There is a high demand for computer assisted software solution for aquaculture research in early detection and recognition of fish population. There exist several companies who have introduced fish detection technologies into the market. Although able to count the number of larvae with a high accuracy rate, the fish counter software's may encounter difficulties when detecting smaller larvae's and ants in very early stage of birth period. The main aim of the paper is to propose a framework using machine learning techniques that can be of low cost and efficient system for fish counting and growth study. The expected final result will be a complete preliminary prototype with basic camera setup, focus on larval fish. medium term, improve camera setup and quality; focus on larval and juvenile fish. For the Long term, full fish growth tracking and data mining is implemented. The proposed research in this paper will assist the Malaysian fisheries department to have accuracy on detecting the larvae, juvenile and ants in the hatcheries.

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Automatic age estimation from facial images is an exciting machine learning topic that has attracted researchers’ attention over the past several years. Numerous human–computer interaction applications, such as targeted marketing, content access control, or soft-biometrics systems, employ age estimation models to carry out secondary tasks such as user filtering or identification. Despite the vast array of applications that could benefit from automatic age estimation, building an automatic age estimation system comes with issues such as data disparity, the unique ageing pattern of each individual, and facial photo quality. This paper provides a survey on the standard methods of building automatic age estimation models, the benchmark datasets for building these models, and some of the latest proposed pieces of literature that introduce new age estimation methods. Finally, we present and discuss the standard evaluation metrics used to assess age estimation models. In addition to the survey, we discuss the identified gaps in the reviewed literature and present recommendations for future research.