(Fall 2012) Face Segmentation Standards

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The purpose of this study is to test multiple face standards to determine Equal Error Rates (EER) amid the facial standard tests preformed. This study will help determine if one face sample can be interoperable amongst multiple standards. Removing facial features from a cropped image raises the EER, lowering the effectiveness of the facial recognition.

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(Fall 2012) Face Segmentation Standards

  1. 1. FACE SEGMENTATION STANDARDS David Shatzer, Douglas Vaisnoras, Blake Rathburn, Michael Brockly, Stephen Elliott Overview The purpose of this study is to test multiple face standards to determine Equal Error Rates (EER) amid the facial standard tests preformed. This study will help determine if one face sample can be interoperable amongst multiple standards. Removing facial features from a cropped image raises the EER, lowering the effectiveness of the facial recognition. Facial Standard Tests Performed • UNCROPPED IMAGES • FIND-BEST PRACTICE • ISO-FRONTAL • ISO-TOKEN • TIGHT Relevancy • Potential international acceptance, one that works in the U.S. will work globally • Saves space, time and money by unifying standards • Multiple accepted standards EER Defined The equal error rate is the point where the probability of false acceptance is equal to the probability of false verification. EER is used as a metric because it is non-discriminating. Standards Comparison EER Table Analysis The variation in the EER is due to how the matcher analyzes the different images. Each standard except for the TIGHT standard is the same initial images just cropped and matched with different variables. The TIGHT standard has a higher EER because the image is heavily zoomed in. This makes it harder for the matcher to recognize a face resulting in a higher EER. UNCROPPED ISO-FRONTAL TIGHT Conclusion From our analysis, keeping the original image without distorting the image results in a lower EER. When removing facial feature from an image, the matcher has difficulty locating the facial features. This results into a higher EER. Recommendations • If one crops an image, the cropped image should not remove facial features • Universal acceptance is challenging, however should be strived for • If one crops an image to a standard, keep the original proportions • When changing the image dimensions, use spacers to ensure image integrity

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