# Tightly-Coupled Data Compression for Efficient Face Alignment
## 1 Introduction
The main reasons are as follows: (1) The deep learning model is usually in the order of 100 MB, which is too big for mobile applications. (2) The computational cost is still quite high and can hardly achieve real-time performances on mobile platforms. (3) Deep learning model need huge amounts of training data, which are not easy to collect for the face alignment task. (4) The training process of deep learning models is tricky without the help of open source implementations from the authors. Figure 1: Face alignment example with the supervised descent method (SDM) algorithm [6]. Firstly, the face region is automatically detected in the image [11]. Then, features are localized within the detected face region. (Image courtesy of [12]). As mentioned above, SDM achieves satisfactory results while having a relatively low computational cost. However, the size of the trained model for SDM can be more than 80 MB, which is still too large for a commercial mobile application. Unfortunately, traditional lossless compression technology such as entropy coding [25] cannot achieve a high enough compression rate to meet the needs of mobile applications. Lossy compression technology is widely used in video encoding areas. State-of-the-art methods such as HEVC (High Efficiency Video Coding) can reach a very high compression rate with good visual quality [26]. Unfortunately, this kind of technology heavily depends on the block motion estimations between consecutive frames in time domains, which are obviously unavailable in our work. Recently, research about deep learning network compression has gradually emerged. Reference [27] proposed new kinds of convolutional operations to reduce parameters. Reference [28] compressed the network by pruning unimportant filters according to weight analysis. References [29; 30] transferred the weights into binary values in order to reduce the size of the model. Instead of transferring the weights into binary values, [PERSON] et al. proposed a method to reduce the precision of weights into ternary values [31]. This method avoids most accuracy degradation. [PERSON] et al. proposed depth-wise separable convolutions architecture so that traditional 3D convolutions can be broken into 2D convolutions [32]. This makes it suitable for mobile applications. Based on [32], shortcut technology was introduced in [33]. Furthermore, linear bottlenecks were used instead of Relu [34] in order to keep the features. Most of the above methods are aimed at specific tasks or network structures such as image classification and image segmentation. To the best of our knowledge, there is no compression architecture that can be applied directly to face alignment networks. In this paper, a tightly-coupled data compression method for the trained model of supervised descent method (SDM) that can reduce the size to less than 1/5 of the original size without obvious performances loss is proposed. This method opens the gates to mobile applications using SDM based face alignment technology. The remainder of the paper is organized as follows: Section 2 briefly describes the main procedure of the SDM algorithm [6] for the sake of completeness and clarity; Section 3 explains our proposed method in detail; Section 4 demonstrates some detailed algorithm implementations in the proposed method; Section 5 shows both qualitative and quantitative experimental results; Section 6 draws conclusions.