Research Article
Face Alignment Algorithm Based on an Improved Cascaded Convolutional Neural Network
Table 1
Design of the CCNN algorithm.
| Stage 1 | Input: X (face images with face frames.) | Step 1: resize X to (39393) | Step 2: after 2 sets of convolutional, pooling layer, and 2 fully connected layers, generate a candidate set of key points | Step 3: same as step 2; generate candidate face key point coordinates | Output: y1 (face image with 5 weighted key points) | Stage 2 | Input: use 2 different windows with shake to crop y1 to obtain 10 partial face images | Step 1: similar to stage1-step2; generate a candidate set of key points | Step 2: every 2 CNNs colocate a key point | Output: y2 (face image with 5 weighted key points) | Stage 3 | Input: use 2 smaller different windows with shake to crop y2 to obtain 10 partial face images | Step 1: similar to stage2-step1; generate a candidate set of key points | Step 2: every 2 CNNs colocate a key point | Output: y3 (face image with 5 weighted key points) |
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