## 1 Introduction  
Higher economic growth drives increasing energy consumption, and 50% of housing consumption is generated by heating, ventilation and air conditioning (HVAC) systems [1; 2]. Furthermore, one of the most important reasons for energy waste is that the actual thermal requirements of indoor occupants are ignored, with the result that overheating and overcooling occur often. Fortunately, real-time thermal comfort perception can provide useful signals to HVAC systems for achieving energy saving and human-centered intelligent control. Therefore, many researchers have been studying thermal comfort measurements for indoor environments in recent decades. Many methods were generated, including the questionnaire survey method [3; 4; 5], environmental measurement method [6; 7] and contact measuring method of human body physiological parameters [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. In recent years, the semi-contact measuring method [19; 20] and contactless measuring method [21; 22; 23] for human body physiological parameters were also generated. For example, in references [19; 20], an infrared sensor was fixed on the frame of eyeglasses in order to measure skin temperature. In reference [21], a normal vision sensor was also used for measuring skin temperature and two non-linear models were trained. In references [22; 23], Kinect was used for recognizing human poses or indoor locations, and then human thermal comfort and dynamic metabolism were estimated, respectively. All these methods are meaningful attempts. However, due to the challenges of measuring thermal comfort which are (1) skin subtleness variation [24], (2) inter-individual differences [14; 25] and (3) temporal intra-individual differences [14; 26], there is still no satisfactory method for perceiving human thermal comfort.  
To overcome the aforementioned challenges, the skin sensitivity index (SSI) was defined in this paper. The SSI is strongly related to skin temperature. A contactless measuring method of skin temperature based on SSI and Deep Learning was proposed, hereinafter referred to as NISDL. Two different deep learning methods of NISDL have been designed and trained, respectively, which are NISDL methods I and II. The main difference between them is that the location of SSI participation in the neural network training is not the same. A total of 1.44 million images were collected for 16 Asian female subjects, and this 'big data' was used for algorithm validation.  
The main contributions of this paper are:  
1. The skin sensitivity index (SSI) was proposed for describing individual sensitivity of thermal comfort, and the index was combined with skin images for deep learning network training.
2. A novel contactless measuring algorithm (NISDL) based on SSI was proposed, with two different frameworks of NISDL having been designed for real-time thermal comfort measurement.
3. A deep learning algorithm without SSI was also generated and trained. Two comparisons were made: (1) comparison between data-driven methods (deep learning) and model-driven methods (linear models); (2) comparison of measuring effects in the case of SSI participation in training and non-participation in training.  
The rest of this paper is organized as follows. Section 2 introduces the related work about thermal comfort. In Section 3, the research methods, including SSI computation, subjective experiments and NISDL methods, are introduced. The results and discussion are shown in Sections 4 and 5. Finally, Section 6 gives the conclusion.