Fingerprint Recognition using Gabor wavelet in MapReduce and Spark

Fingerprint recognition is one of the most popular biometric recognition methods nowadays. It is applicable in many areas including time recorder systems, criminal tracking, authentication and system security. However, one of the challenges to current traditional methods is the dependence on the minutiae extraction and recognition time. Hence, the limitations of these methods are that they do not effect to recognition in a large data environment. In addition, the processing of input image is very important for improving the accuracy of the recognition process. MapReduce technique is used in exploring and analyzing of large data that can not be processed on classical techniques due to some constraints on computer resources such as processing capability, memory, etc. We performed parallel processing in feature extraction and recognition with the MapReduce model in a Spark environment. we have also compared the accuracy and the runtime of our method before and after using MapReduce in the Spark. The experimental results show that the proposed method has achieved the automatic and effective fingerprint recognition.