Recurrent Convolutional Shape Regression

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Cascaded regression methods dominate face alignment.
• These methods have limitations: independent learning of shape increments and reliance on generic features (SIFT, HOG).

Purpose of the Study:

• To propose a novel Recurrent Convolutional Shape Regression (RCSR) method to overcome limitations of existing face alignment techniques.
• To enable joint learning of shape increments and task-specific features.

Main Methods:

• Formulated face alignment as a recurrent process using a recurrent neural network with a gated recurrent unit.
• Combined convolutional neural networks (CNNs) with recurrent neural networks (RNNs) to learn task-specific features, avoiding hand-crafted ones.
• Employed convolutional gated recurrent units processing feature tensors to preserve spatial structure.

Main Results:

• The proposed RCSR method demonstrated superior performance compared to state-of-the-art methods in experimental evaluations.
• Showcased the effectiveness of learning a single end-to-end model for face alignment.

Conclusions:

• RCSR effectively addresses limitations of traditional cascaded regression methods in face alignment.
• Jointly learning shape increments and task-specific features via an end-to-end model leads to improved face alignment accuracy.