1.  Detecting system  Training system Human Emotions Estimation by Adaboost based on Jinhui Chen, Tetsuya Takiguchi, Yasuo Ariki （ Kobe University ） User's Facial Expression and Average Face from Different Directions Overview  Background TV programs customer content recommendation automatic analysis needs to collect the data of user‘s facial expression. The expression of customer’s face is directly related to sales content recommendation. To understand human emotions is able to improve robot cognitive and interactive abilities Proposed method Features Extraction Experiment  Problems & Approaches problem1 When the user’s head significantly rotating, there would lead to be obvious errors. aproaches1 Combine the three-dimensional average face (3DAF) with Adaboost problem2 The cost of processing data and time is high by the real-time detecting aproaches2 The average face models are projected into the 8-bit gray image, which is intend of the original data.  Conventional methods Adaptive Boosting [1] (Adaboost) is a basic method widely used in face feature extraction and recognition. The method is operated easily, and its classification is quite precise. Though it is sensitive to noisy data and outliers. What’ s more, it is not good at processing the event that is lack of previous train. So it need to be used in conjunction with many other algorithms to improve the performance. [1]Yoav Freund and Robert Schapire,1995 Boost training Cut out features The face marked feature points Be updated by each iteration Flowchart Input video Estimate emotions Obtain the face region Recover into the 3D models create average face by 3D features create average face by 3D features The emotion features data The face features data The system includes 2 parts, the 1st one is training system, during this stage face features and emotion features data is extracted and saved as database individually. The other stage the face expressions are classified and processing cost is cut down Recover 3D features The original face data is recovered as 3D model to get more features points We adjust the dif- ference between the model data and the original data by the following function,to control the error. E I : control factor(≤0.001) I o : the original face data I m : 3D model data U T : Projection vector Average face creation Obtained the 3D features, the coordinates S i and RGB value T i are got easily. Taking use of the data to calculate their average value, : :previous i-th iteration average value : :adjustment factors N :total of elements The average model is projected into 8- bit gray image, we will get final average face features, which is used as emotion classification. 2D features 3D features Experiment information Trained samples: 20X3X12+75 The person tested: 2 Emotion groups : Natural(Nau), Happy(Hap), Unhappy(Unh) Data Explanation The classification effects using two methods’ features are compared; The correct rates under conditions of two motion states are compared. Head keeps static Head freely rotates Conclusion In our research, we proposed a novel method for improving emotions estimation. Our experiments have shown that our approach improved the emotions classification rate, specifically the head was freely rotating. SDAM [2] draft [2] T. Wang et al.,1995 Procrustes analysis [3] [3] C. Goodall,1991 ISS-P-252