Lead R&D Engineer @ Sightcorp https://speakerdeck.com/rjbruin 2019-05-07 10:27:36 -0400 Originally presented at "The latest in AI @Bol.com", May 8th 2019 (https://www.meetup.com/bol-com-techlab/events/260444588/). Face analysis has recently become a topic of interest for both press and science due to the implications that this technology has for our own life and privacy. Indeed, our face is inherently related to our identity. This information can be used for identification purposes, such as unlocking our phone, but it can also be used to extract personal information, such as age, gender and emotion, for commercial or statistical purposes. Therefore, given the delicate aspects of this technology, the problem has to be treated carefully. From a Machine Learning point of view, face analysis can be defined as, given an image, the extraction of necessary features to detect a face and then to identify personal information from it. Face detection needs to be the first step of any face analysis system, while auxiliary tasks such as age, gender, pose, smile classification, or even identification can follow from the extracted detection. How do we properly detect a face? How do we learn these face-related features? In this talk, I will show how Deep Learning has revolutionized computer vision for face detection and I will review the common architectures that can be used to tackle this problem. In particular, I will speak about the Single-Shot Multibox Detector (SSD) architecture as well as the implications of varying image resolutions on the detection of faces. Originally presented at "The latest in AI @Bol.com", May 8th 2019 (https://www.meetup.com/bol-com-techlab/events/260444588/). Face analysis has recently become a topic of interest for both press and science due to the implications that this technology has for our own life and privacy. Indeed, our face is inherently related to our identity. This information can be used for identification purposes, such as unlocking our phone, but it can also be used to extract personal information, such as age, gender and emotion, for commercial or statistical purposes. Therefore, given the delicate aspects of this technology, the problem has to be treated carefully. From a Machine Learning point of view, face analysis can be defined as, given an image, the extraction of necessary features to detect a face and then to identify personal information from it. Face detection needs to be the first step of any face analysis system, while auxiliary tasks such as age, gender, pose, smile classification, or even identification can follow from the extracted detection. How do we properly detect a face? How do we learn these face-related features? In this talk, I will show how Deep Learning has revolutionized computer vision for face detection and I will review the common architectures that can be used to tackle this problem. In particular, I will speak about the Single-Shot Multibox Detector (SSD) architecture as well as the implications of varying image resolutions on the detection of faces. Wed, 08 May 2019 00:00:00 -0400 https://speakerdeck.com/rjbruin/efficient-face-detection-through-deep-learning https://speakerdeck.com/rjbruin/efficient-face-detection-through-deep-learning