This paper proposes a universal framework, termed as Multi-Task Hybrid Convo-lutional Neural Network (MHCNN), for joint face detection, facial landmark detection, fa-cial quality, and facial attribute analysis. MHCNN consists of a high-accuracy single stage detector (SSD) and an efficient tiny convolutional neural network (T-CNN) for joint face de-tection refinement, alignment and attribute analysis. Though the SSD face detectors achieve promising results, we find that applying a tiny CNN on detections further boosts the detect-ed face scores and bounding boxes. By multi-task training, our T-CNN aims to provide five facial landmarks, facial quality scores, and facial attributes like wearing sunglasses and wearing masks. Since there is no public facial quality data and facial attribute data as we need, we contribute two datasets, namely FaceQ and FaceA, which are collected from the Intet. Experiments show that our MHCNN achieves face detection performance compara-ble to the state of the art in face detection data set and benchmark (FDDB), and gets rea-sonable results on AFLW, FaceQ and FaceA.