This thesis proposes a face detection and facial landmark estimation method based on the multi-task cascaded convolutional neural networks (MTCNN) and discusses the effects of multi-task learning on the accuracy of the face bounding box regression vector and facial landmark locations. The proposed model consists of three convolutional neural networks, namely proposal network (P-Net), refine network (R-Net), and output net (O-Net). First, P-Net performs face bounding box detection and calculates the bounding box regression vector to calibrate the candidate boxes. Moreover, P-Net employs the non-maximum suppression (NMS) method to merge the overlapping candidate boxes. The remaining bounding boxes are input to the R-Net, which further rejects a large number of incorrect candidate boxes. Similar to the P-Net, R-Net also uses the bounding box regression vector to calibrate candidate boxes and merge candidate boxes by NMS. The remaining bounding boxes of the R-Net are sent to the O-Net which is trained based on both the face bounding boxes and facial landmark localization information (left eye, right eye, nose, left mouth corner, right mouth corner). Finally, the Q-Net outputs the detected face bounding boxes and the estimated facial landmark locations. In the training process, we use the multi-task learning method and online hard sample mining technique to improve performance. The WIDER FACE dataset is employed to train the MTCNN for the task of face detection and the CelebA dataset is adopted to train the same network for the task of facial landmark estimation. We use the easy, medium, and hard validation sets in the WIDER FACE dataset to verify the performance of the MTCNN and draw the precision-recall curve. For the easy, medium, and hard validation sets, the recall rates of our trained MTCNN are 0.695, 0.67, and 0.402, and the precision rates are 0.6,0.78, and 0.8, respectively, which are better than those results obtained by Faceness, Two-stage CNN, and ACF methods. To evaluate the performance of MTCNN on the task of facial landmark estimation, we use the validation sets in the Deep Convolutional Network Cascade for Facial Point Detection (DCNN) dataset and the CelebA dataset to calculate the mean error and failure rate. For the DCNN validation set, the mean error (in terms of percentage) of the left eye, right eye, nose, left mouth corner, and right mouth corner are 10.89, 11.8, 14.2, 15.6, 16.06, respectively. The failure rate (in terms of percentage) of the left eye, right eye, nose, left mouth corner, and right mouth corner are 27.71, 28.11, 45.38, 53.82, 54.22, respectively. For the CelebA validation set, the mean error (in terms of percentage) of the left eye, right eye, nose, left mouth corner, and right mouth corner are 10.60, 10.95, 13.36, 14.15, 14.44, respectively. The Failure rate (in terms of percentage) of the left eye, right eye, nose, left mouth corner, and right mouth corner are 27.72, 28.67, 43.30, 49.96, 51.04, respectively.