Biometric applications become paramount across private sectors, industry, as well as government agencies. As large amount of data being collected from many different sources, managing such volumes of data and developing efficient and effective large-scale operational solutions are becoming a concern. For example, real-time identification of individuals with the purpose of allowing or denying their access to specific system or resource is challenging from the performance point of view. In addition, processing large amount of data would definitely consume a significant amount of energy. The Single-chip Cloud Computer (SCC) is an experimental processor created by Intel Labs. In this paper we employ SCC, which supports dynamic frequency and voltage scaling (DVFS), to investigate the power-aware computing and performance enhancement of an iris matching algorithm on such many-core architecture. This biometric application contains a large degree of parallelism that we can exploit by porting it onto the SCC. Results in terms of performance, power, energy, energy delay product (EDP), and power per speedup (PPS) metrics of executing the iris matching application under different number of cores, frequency, and voltage settings of the SCC platform are presented. We also analyze how the results for these metrics vary as we change these parameters.