Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces
This work addresses robust facial landmark detection for computer vision applications, presenting an incremental improvement through aggregation techniques.
The paper tackles facial landmark detection under large shape and appearance variations by aggregating predictions from geometrically manipulated face images, achieving superior performance compared to state-of-the-art methods on benchmark datasets AFLW, 300-W, and COFW.
In this work, we present a practical approach to the problem of facial landmark detection. The proposed method can deal with large shape and appearance variations under the rich shape deformation. To handle the shape variations we equip our method with the aggregation of manipulated face images. The proposed framework generates different manipulated faces using only one given face image. The approach utilizes the fact that small but carefully crafted geometric manipulation in the input domain can fool deep face recognition models. We propose three different approaches to generate manipulated faces in which two of them perform the manipulations via adversarial attacks and the other one uses known transformations. Aggregating the manipulated faces provides a more robust landmark detection approach which is able to capture more important deformations and variations of the face shapes. Our approach is demonstrated its superiority compared to the state-of-the-art method on benchmark datasets AFLW, 300-W, and COFW.