Implications of Government Sponsored Social Security and Welfare Schemes on Socioeconomic Status of Small Scale Marine Fisherfolk in Kerala

A socio-economic research is required as an attempt to address the socio-economic issues facing small-scale fisheries. A study of the socio economic conditions of small-scale fishermen is a prerequisite for good design and successful implementation of effective assistance Programmes. It will provide an overall pidure of the structure, activities and standards of living of small-scale fisherfolk The study is confined to the coastal districts of Ernakulam, Thrissur and Malappuram districts. It also gives a picture of socio-economic conditions of the fisher folk in the study area. The variables that may depict the standard of living of the small-scale fisherfolk are occupational structure, family size, age structure, income, expenditure, education, housing and other social amenities. It attempts to see the asset creation of the fisherfolk with the help of government agencies, and the nature of savings and expenditure pattern of the fisherfolk. It also provides a picture of the indebtedness of the fisherfolk in the study area. The study analyses the schemes implemented by the government through its agencies, like Fisheries Department, Matsyaboard, and Matsyafed; and the awareness of fisherfolk regarding these schemes, their attitude and reactions, the extent of accessibility, and the viability of the schemes.

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems