Basically, I used a Generative Adversarial Network to create faces from a Tensorflow Hub pretrained GAN. Then I interpolated between these faces as vectors on a tensor, creating blends of faces from the same demographic categories. Two example of this are seen here (Bruno Mars and George Eads):<\/p>\n\n\n\n Each face is increasingly dissimilar for the first face. By attempting to match the celebrity face at the top, we can begin to understand disparities between demographics in facial recognition, a topic which has been called into question by ANSI, the American National Standards Institute. Each face down the interpolation is less like the actual GAN generated celebrity, and should define a boundary within the interpolation space, which represents precision of the adversarial images. Ideally, a demographically fair model will draw the same boundaries across these distortions regardless of demographic and between demographic classes. <\/p>\n\n\n\n The demographic categories for which data was generated were: Female and Male, African and Caucation with four total categories, although this could be expanded with the code included.<\/p>\n\n\n\n All of the data used in this paper is available in the Github Repository here:<\/p>\n\n\n\n https:\/\/github.com\/davidgabriel42\/Dataset-CS704-Data_Augmentation-Demographics-Race<\/a><\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" So I’ve released my paper on Facial Recognition I did for my Digital Forensics course. This course was taught by Det. Watson with the Reno PD, and used a lot of state of the art forensic tools. It is part of the UNR cybersecurity push that has been great to be a part of. I… Read More »Final Paper: CS 704 Digital Forensics<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-43","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/posts\/43","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/comments?post=43"}],"version-history":[{"count":1,"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/posts\/43\/revisions"}],"predecessor-version":[{"id":48,"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/posts\/43\/revisions\/48"}],"wp:attachment":[{"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/media?parent=43"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/categories?post=43"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/david-gabriel.com\/blog\/wp-json\/wp\/v2\/tags?post=43"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/david-gabriel.com\/blog\/wp-content\/uploads\/2021\/03\/image.png\")
<\/a><\/figure>\n\n\n\n