diff --git a/face/ArcFace.py b/face/ArcFace.py
new file mode 100644
index 0000000..e69de29
diff --git a/face/face_embedding.py b/face/face_embedding.py
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index 0000000..e69de29
diff --git a/face/main.py b/face/main.py
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--- /dev/null
+++ b/face/main.py
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+
+from yoloface import face_detector
+from util.distance import findEuclideanDistance
+from itertools import combinations
+from util.VideoCapture import VideoCapture
+from util.plot import *
+import torch
+from similarImageFinder_functions import *
+from verifier import FaceVerifier, VGGFace, ArcFace
+
+
+class SimilarImageFinder:
+    def __init__(self, detector, verifier, video_name, video_path, period=30, video_capture=True):
+        self.video_name = video_name
+        if video_capture:
+            # 영상 -> 이미지
+            VideoCapture(period, video_name, video_path)
+            # 같은 프레임의 이미지를 중에서 적어도 2번 이상 사람이 감지된 프레임만 추출해서 data frame 생성
+            df = self._make_df_detection_by_detector(detector, video_name)
+        else:
+            df = pd.read_parquet(f'./dataset/{video_name}/detection_data/yoloface_data.parquet')
+        # 유사한 이미지 탐색
+        similarity_list = self.find_similarity_images(df, verifier)
+        print(similarity_list)
+        # 이미지 출력
+        plot_by_cv2(similarity_list, video_name, 30)
+
+
+    def _make_df_detection_by_detector(self,detector, video_name):
+        """
+        Face Detection 데이터 프레임을 만드는 함수입니다.
+        :param: detector ['opencv', 'ssd', 'mtcnn', 'retinaface', 'yoloface]
+        :return:
+        """
+        if detector == 'yoloface':
+            try:
+                torch.tensor(1).cuda()
+                # de
+                yolo_detector = face_detector.YoloDetector(target_size=720, gpu=0, min_face=90)
+                print('gpu 사용')
+                df_detection = detect_images_by_gpu(yolo_detector, video_name, batch_size=16)
+            except:
+                yolo_detector = face_detector.YoloDetector(target_size=720, gpu=-1, min_face=90)
+                print('cpu 사용, 시간이 오래걸리기 때문에 gpu를 이용하는 것을 권장합니다.')
+                df_detection = detect_images_by_cpu(yolo_detector, video_name)
+        else:
+            print('아직 미구현')
+        # frame_num 순으로 정렬(시간 순으로 정렬됨)
+        df_detection = df_detection.sort_values(by='frame_num')
+        # filter 된 dataframe
+        df_filtered = filter_df(df_detection)
+        # 데이터프레임 저장
+        df_filtered.to_parquet(f'./dataset/{video_name}/detection_data/yoloface_data.parquet',engine='pyarrow')
+
+        return df_filtered
+
+
+    def find_similarity_images(self,df_filtered, verifier='VGGFace'):
+        similarity_list = []
+        if verifier == 'VGGFace':       # Face Verification 을 위한 VGGFace Model 불러오기
+            model = VGGFace.loadModel()
+        elif verifier == 'ArcFace':
+            model = ArcFace.loadModel()
+
+        for frame in df_filtered.index.unique():
+            df_temp = df_filtered.loc[frame]
+            # 감지된 사람수
+            detect_person_num = df_temp['detect_person_num'].iloc[0]
+            # 영상 번호 리스트
+            video_num_list = list(df_temp['video_num'])
+            # 선택된 영상 번호
+            selected_video = None
+            # 거리
+            dis_min = 100000000
+
+            for selected_video_nums in list(combinations(video_num_list, 2)):
+                videonum1, videonum2 = selected_video_nums[0], selected_video_nums[1]
+                boxes = list(df_temp.loc[(df_temp['video_num'] == videonum1) | (df_temp['video_num'] == videonum2)]['boxes'])
+                imgs_path = [f'./dataset/{self.video_name}/frame/{frame}/{videonum1}.jpg',
+                             f'./dataset/{self.video_name}/frame/{frame}/{videonum2}.jpg']
+
+                # 2개의 이미지에서 가장 큰 얼굴 간의 비율, face verification 을 위한 crop face image
+                area_fraction, crop_face1, crop_face2 = get_max_area_fraction_and_crop_faces(boxes, imgs_path)
+
+                # #Verified Test : 검증 확인
+                # verified = FaceVerifier.verify(crop_face1, crop_face2, model)['verified']
+                # #check not verified face
+                # if not verified: plot_crop_face(crop_face1, crop_face2)
+                # # check verified face
+                # if verified: plot_crop_face(crop_face1, crop_face2)
+
+                # compare area_fraction and face recognition
+                if 0.8 < area_fraction < 1.2 and FaceVerifier.verify(crop_face1, crop_face2, model)['verified']:
+                    landmarks = list(
+                        df_temp.loc[(df_temp['video_num'] == videonum1) | (df_temp['video_num'] == videonum2)]['landmarks'])
+                    
+                    dis = findEuclideanDistance(landmarks[0], landmarks[1], detect_person_num)
+
+                    if dis < dis_min:
+                        dis_min = dis
+                        selected_video = selected_video_nums
+
+            if selected_video is not None:
+                similarity_list.append((frame, selected_video, dis_min))
+
+        similarity_list = sorted(similarity_list, key=lambda x: x[2])
+
+        return similarity_list
+
+
+if __name__ == '__main__':
+    video_path = '/content/drive/MyDrive/HAI/HAI_Project/dataset'
+    # video capture 생략하고 싶으면 False
+    # SimilarImageFinder('yoloface', 'idle_tomboy3', video_path, period=30, video_capture=True)
+    SimilarImageFinder('yoloface', 'ArcFace', 'tomboy1', video_path, period=30, video_capture=False)
+
+if __name__ == '__main__':
+    video_path = '/content/drive/MyDrive/HAI/HAI_Project/dataset'
+    # video capture 생략하고 싶으면 False
+    # SimilarImageFinder('yoloface', 'idle_tomboy3', video_path, period=30, video_capture=True)
+    SimilarImageFinder('yoloface', 'VGGFace', 'tomboy2', video_path, period=30, video_capture=False)
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