300-W test set is aimed to test the ability of current systems to handle unseen subjects, independently of variations in pose, expression, illumination, ...

The 300-W is a face dataset that consists of 300 Indoor and 300 Outdoor in-the-wild images. It covers a large variation of identity, expression, illumination ...

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300-W Challenge is the first event of its kind organized exclusively to benchmark the efforts in the field. The particular focus is on facial landmark ...

It is a rapid (60 s) test that can be performed using a consumer augmented reality headset with integrated eye-tracking, and might conceivably be used remotely ...

The 300w is a face dataset made up of 300 in-the-wild photographs taken inside and outdoors. It encompasses a wide range of identity, expression, lighting, ...

This model is designed to work well with dlib's HOG face detector and the CNN face detector (the one in mmod_human_face_detector.dat).

The temporal eyelid movement patterns extracted from the samples in the database are analyzed by a Siamese neural network. The achieved results of 98.20% ...

Implementation of facial landmarks detection using pytorch on iBUG 300W dataset. Why Xception Net? Because it provides satisfactory accuracy.

o Purpose: The ibug 300W face dataset contains ''in-the-wild'' images collected from the internet. o Properties: Properties. Descriptions. # of subjects.