{"action":"created","issue":{"url":"https://api.github.com/repos/saebrahimi/Emotion-Recognition-RNN/issues/2","repository_url":"https://api.github.com/repos/saebrahimi/Emotion-Recognition-RNN","labels_url":"https://api.github.com/repos/saebrahimi/Emotion-Recognition-RNN/issues/2/labels{/name}","comments_url":"https://api.github.com/repos/saebrahimi/Emotion-Recognition-RNN/issues/2/comments","events_url":"https://api.github.com/repos/saebrahimi/Emotion-Recognition-RNN/issues/2/events","html_url":"https://github.com/saebrahimi/Emotion-Recognition-RNN/issues/2","id":154637912,"number":2,"title":"some question about pre-processing","user":{"login":"HeGaoYuan","id":16939557,"avatar_url":"https://avatars.githubusercontent.com/u/16939557?v=3","gravatar_id":"","url":"https://api.github.com/users/HeGaoYuan","html_url":"https://github.com/HeGaoYuan","followers_url":"https://api.github.com/users/HeGaoYuan/followers","following_url":"https://api.github.com/users/HeGaoYuan/following{/other_user}","gists_url":"https://api.github.com/users/HeGaoYuan/gists{/gist_id}","starred_url":"https://api.github.com/users/HeGaoYuan/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/HeGaoYuan/subscriptions","organizations_url":"https://api.github.com/users/HeGaoYuan/orgs","repos_url":"https://api.github.com/users/HeGaoYuan/repos","events_url":"https://api.github.com/users/HeGaoYuan/events{/privacy}","received_events_url":"https://api.github.com/users/HeGaoYuan/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":3,"created_at":"2016-05-13T05:40:35Z","updated_at":"2016-05-17T00:29:09Z","closed_at":null,"body":"Hi,\r\nIn your PreProcessing folder, in the mapAll2FER.m file, https://github.com/saebrahimi/Emotion-Recognition-RNN/blob/master/PreProcessing/mapAll2FER.m#L18\r\nyou comment  \r\n\r\n> %add border of 32 for TFD which results in 160 image size\r\n\r\n\r\n but actually you add **48** to the pavg"},"comment":{"url":"https://api.github.com/repos/saebrahimi/Emotion-Recognition-RNN/issues/comments/219587688","html_url":"https://github.com/saebrahimi/Emotion-Recognition-RNN/issues/2#issuecomment-219587688","issue_url":"https://api.github.com/repos/saebrahimi/Emotion-Recognition-RNN/issues/2","id":219587688,"user":{"login":"saebrahimi","id":4037904,"avatar_url":"https://avatars.githubusercontent.com/u/4037904?v=3","gravatar_id":"","url":"https://api.github.com/users/saebrahimi","html_url":"https://github.com/saebrahimi","followers_url":"https://api.github.com/users/saebrahimi/followers","following_url":"https://api.github.com/users/saebrahimi/following{/other_user}","gists_url":"https://api.github.com/users/saebrahimi/gists{/gist_id}","starred_url":"https://api.github.com/users/saebrahimi/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/saebrahimi/subscriptions","organizations_url":"https://api.github.com/users/saebrahimi/orgs","repos_url":"https://api.github.com/users/saebrahimi/repos","events_url":"https://api.github.com/users/saebrahimi/events{/privacy}","received_events_url":"https://api.github.com/users/saebrahimi/received_events","type":"User","site_admin":false},"created_at":"2016-05-17T00:29:09Z","updated_at":"2016-05-17T00:29:09Z","body":"If your dataset is different from Emotiw15 or FER, you need to run a keypoint detector such as the one I referred to here: https://github.com/saebrahimi/Emotion-Recognition-RNN/tree/master/PreProcessing\r\nIf your dataset is cropped tighter than FER then you need to add a border to roughly have same location for face (the line you initially asked about).\r\n\r\nWhy mean shape:\r\n- you don't need perfect alignment for CNNs but we found that an approximate alignment as done here helps with limited dataset size.\r\n- it helps if keypoint detector fails on part of your dataset and you don't need to detect them at test time as you have a fixed transformation pre-computed on train set.\r\n- a simple work around is to hand-label a small subset of your training data and average the keypoint's location and use it as mean-shape of your dataset. (we only map mean-shape to mean-shape to merge different datasets, if you have only one dataset you can skip this step)\r\n"}}