Face Detection And Recognition ForDistributed SystemsMeng Lin and Ermin Hodžić

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Motivation

• Security systems•Digital cameras, adjustments• Social networks•Marketing

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Face Detection

• Locating and extracting faces in images•Rectangles as output

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Face Recognition

• Identifying person on image• Finding closest match

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MapReduce model

•Parallel model• Framework for distributed systems•Hadoop local filesystem•Amazon Elastic MapReduce

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Solution: Face Detection

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Solution: Face Detection

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Solution: Face Detection

•Partitions based on face scale

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Solution: Face Recognition

•Distribution of recognizers•Recognizing in parallel•Reduce the most confident result

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Solution: Face Recognition

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Hadoop Image Handling

• Images as text•Collection of images in a big file•Utilize Hadoop default input format

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Hadoop Image Handling

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Evaluation

• Segmented Face Detection

•Raw OpenCV Face Detection

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Evaluation

• Face recognition

•More nodes = slower• Input and data transfer overhead• Jobs computationally cheap

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Conclusion

• Easily scalable parallel mode•Generalized framework• OpenCV just one sample tool

•A lot of communication• Low utilization of processors

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