# A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition
## 2 Related Work
### Scale-Invariant Feature Transform  
Feature detection and image matching are the most critical tasks in the field of machine vision. Because they have different computational efficiency or accuracy depending on which feature detector and descriptor extraction algorithm are used, it is critical to choose a suitable algorithm according to feature matching tasks [43]. Algorithms such as SIFT, speeded up robust feature (SURF), and binary robust invariant scalable keypoint (BRISK) are mainly used, and each algorithm has differences in performance [44]. SIFT is known to be most robust in feature detection and matching. In particular, its robustness is apparent \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Work** & **Author** & **Year** & **Description** \\ \hline A hybrid approach to building face & & & This study proposed an automatic hairstyle recommender system according to face type. \\ shape classifier for hairstyle recommender system & [PERSON], et al. [39] & 2019 & Feature extraction was performed from images through a deep learning model and classified based on the support vector machine. \\ \hline Enhancing Retailer Marketing with an & & & This study proposed a facial recognition integrated recommender system to address profiling problems (user information recognition) in retail stores. Based on this, the user’s information (gender, age, and facial expression) is predicted by recognizing the face. \\ \hline Enhancing recommender systems for & [PERSON], et al. [41] & 2016 & This study proposed a system that supplemented the TV content recommender system by detecting and recognizing the facial emotions of users watching TV. Through the proposed system, a task was conducted to predict age, gender, and emotion. \\ \hline Music Recommendation System Based on Facial Emotion Recognition & [PERSON], et al. [42] & 2020 & This study used facial recognition technology to predict user emotions and developed a music recommender system. To this end, faces were recognized using AdaBoost, and facial emotions were classified into eight categories through an SVM. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work. in terms of image scale, rotation, and affine transformation. SURF is based on SIFT and changes filter size instead of image pyramid. SURF is robust in relation to image scale and rotation, but weak for affine transformation; another advantage is its relatively fast calculation speed compared with SIFT [45]. BRISK is a binary corner detection algorithm robust to changes in image scale and rotation. It has the advantage of enabling a faster calculation than SIFT and SURF [46]. According to previous studies, SIFT was a little disadvantage in terms of speed but had excellent accuracy [43, 47]. Therefore, we perform feature extraction by applying the SIFT algorithm to accurately and effectively extract features included in user facial expressions. The SIFT algorithm is an image descriptor developed for image-based matching and recognition [48, 49]. The image descriptor is a means of expressing images such as keyframe and face, and it is used for image comparison when extracting scene transitions and searching for similar images. The SIFT algorithm extracts characteristic points unique to images, and it is also a strong algorithm in the detection of many environmental changes such as image size change, deformation, rotation, lighting change, and so on. The SIFT algorithm has proven to be useful for experimentally measuring the similarity between images, matching images, and object recognition [50]. The SIFT algorithm consists of four main processes: scale-space extrema detection, orientation assignment, keypoint localization, and keypoint descriptor. Scale-space extrema detection creates scale-space and detects extrema. Scale-space refers to images obtained by creating an image pyramid by resizing an original image with various scales and then using the image pyramid's octave image as a growing Gaussian blur scale factor.