With the advance of facial recognition technology, many related applications such as the clearance of specific facilities, air port security, video camera surveillance, and personnel recognition. To maximize working efficiency and reduce human resource, the platform used for facial recognition should possess both low cost, multimedia performance, and the ease of use. Among the list of available platforms, a IBM CELL multi-core based platform that features the aforementioned advantages is used to manifest our work. To meet the demand of recognition accuracy, a recognition algorithms using features low error rate and regular data patterns are adopted. These algorithms are carried out in two parts: Modified Census Transform (MCT) and hypotheses of human facial calculation. The multi-point average value required by the MCT is obtained through parallel processing, and potential improvement in recognition efficiency is possible if wider data paths are used. A PlayStation 3 (PS3) platform equipped with the IBM CELL multi-core processor is used in this thesis. The IBM CELL multi-core processor consists of a PowerPC Processor Element (PPE) and 8 Synergistic Processor (SPE), which forms a heterogeneous multi-core system. This system is capable of parallelizing thread-level and data-level data words, which can meet the demand of high data bandwidth and data parallelization. By using this platform to accelerate the processing of facial recognition, simulation results suggest that the execution efficiency is improved by 24 times when compared with a single core SPE. The simulation also reveals that the use of parallelization of processing facial recognition data feasible. In the future, improved algorithms can be applied to improve the accuracy of facial recognition.