Finding ‘Reasons to Stay’ Amidst Issues of Well-Being: A Case Study of Two Underserved Communities in Colombo

This paper attempts to explore the factors that attract and encourage individuals to live in low-income neighbourhoods in Colombo in spite of the many socioeconomic issues that are associated with such communities. Data was collected through 30 face-to-face in-depth interviews with residents from two underserved communities consisting of individuals with different migration experiences. The collected data was then analysed using the three-dimensional well-being model introduced by Pouw and McGregor (2014). The study revealed a situation of material and relational wellbeing intersecting to create a more practical kind of well-being in the communities studied. Of the two, material well-being had the strongest power to attract and retain residents in the neighbourhoods while relational wellbeing played a supportive role in terms of pulling people into the community. Subjective well-being, on the other hand, was identified as the strongest reason with a capacity to push people away from the community. However, this single push factor was not strong enough to overpower the pull effect of material and relational well-being, particularly because of the residents’ low-income status. The material benefits of living in the location facilitated by social ties offered by the neighbourhood kept these residents attracted and attached to these underserved communities

Comparative Analysis of Rotation Invariant Pattern and Uniform Pattern in MMLBP Technique for Face Recognition

Recognizing humans based on one or more physical or behavioral traits is referred as Biometrics. Comparing to the traditional methods to authenticate persons, biometric plays a vital role in the area of human recognition. In the field of biometric, face and palm print recognition seeks more attention for the researchers. The failure of recognition is minimum and also the implementation is easier than more other techniques. In this paper we concentrated on face recognition with Local Binary Pattern(LBP), it is simple and fast to recognize face than more other algorithms. Uniform LBP is used to extract features to recognize the face to authenticate the persons. The “non-uniform” patterns are clustered into one pattern due to this lot of information lost. In order to overcome the heavy data which loss in non-uniform patterns a modified multi-scale LBP histogram algorithm is proposed. Hence, the useful non-uniform information is utilized without any training step with entire information without any data loss. We also compare the mapping methods, rotational invariant pattern and uniform with rotational invariant patterns and .hence evaluate the performance of the mapping methods