The world's simplest facial recognition api for Python and the command line
This report summarizes scanner evidence for ageitgey/face_recognition, a Python project with 56,811 GitHub stars. SynthScan examined 4,699 lines of code across 44 source files, recording 78 pattern matches distributed across 9 syntactic categories. The legacy adjusted pattern signal is 23.3 (Elevated pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 67 finding(s).
Are imports declared and package/API names verified? 10 finding(s).
Are wrappers, guards, and repeated structures necessary? 25 finding(s).
Which changes need focused human review? 78 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 78 distinct pattern matches across 9 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | docs/conf.py | 7 | # This file is execfile()d with the current directory set to its | COMMENT |
| MEDIUM | examples/facerec_from_webcam_faster.py | 35 | # Initialize some variables | COMMENT |
| MEDIUM | examples/blur_faces_on_webcam.py | 13 | # Initialize some variables | COMMENT |
| MEDIUM | examples/identify_and_draw_boxes_on_faces.py | 36 | # Create a Pillow ImageDraw Draw instance to draw with | COMMENT |
| MEDIUM | examples/facerec_from_webcam_multiprocessing.py | 143 | # Create a thread to capture frames (if uses subprocess, it will crash on Mac) | COMMENT |
| MEDIUM | examples/facerec_from_video_file.py | 14 | # Create an output movie file (make sure resolution/frame rate matches input video!) | COMMENT |
| MEDIUM | examples/facerec_from_video_file.py | 30 | # Initialize some variables | COMMENT |
| MEDIUM | examples/find_faces_in_picture.py | 8 | # This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated. | COMMENT |
| MEDIUM | examples/facerec_on_raspberry_pi.py | 25 | # Initialize some variables | COMMENT |
| MEDIUM | examples/find_faces_in_picture_cnn.py | 8 | # This method is more accurate than the default HOG model, but it's slower | COMMENT |
| MEDIUM | examples/find_facial_features_in_picture.py | 12 | # Create a PIL imagedraw object so we can draw on the picture | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | face_recognition/api.py | 124 | def _raw_face_locations_batched(images, number_of_times_to_upsample=1, batch_size=128): | CODE |
| LOW | face_recognition/api.py | 146 | def convert_cnn_detections_to_css(detections): | CODE |
| LOW | face_recognition/face_detection_cli.py | 29 | def process_images_in_process_pool(images_to_check, number_of_cpus, model, upsample): | CODE |
| LOW | face_recognition/face_recognition_cli.py | 71 | def process_images_in_process_pool(images_to_check, known_names, known_face_encodings, number_of_cpus, tolerance, show_d | CODE |
| LOW | tests/test_face_recognition.py | 40 | def test_cnn_raw_face_locations(self): | CODE |
| LOW | tests/test_face_recognition.py | 81 | def test_partial_face_locations(self): | CODE |
| LOW | tests/test_face_recognition.py | 94 | def test_raw_face_locations_batched(self): | CODE |
| LOW | tests/test_face_recognition.py | 104 | def test_batched_face_locations(self): | CODE |
| LOW | tests/test_face_recognition.py | 139 | def test_face_landmarks_small_model(self): | CODE |
| LOW | tests/test_face_recognition.py | 155 | def test_face_encodings_large_model(self): | CODE |
| LOW | tests/test_face_recognition.py | 188 | def test_face_distance_empty_lists(self): | CODE |
| LOW | tests/test_face_recognition.py | 230 | def test_compare_faces_empty_lists(self): | CODE |
| LOW | tests/test_face_recognition.py | 248 | def test_command_line_interface_options(self): | CODE |
| LOW | tests/test_face_recognition.py | 255 | def test_command_line_interface(self): | CODE |
| LOW | tests/test_face_recognition.py | 266 | def test_command_line_interface_big_image(self): | CODE |
| LOW | tests/test_face_recognition.py | 277 | def test_command_line_interface_tolerance(self): | CODE |
| LOW | tests/test_face_recognition.py | 288 | def test_command_line_interface_show_distance(self): | CODE |
| LOW⚡ | tests/test_face_recognition.py | 299 | def test_fd_command_line_interface_options(self): | CODE |
| LOW⚡ | tests/test_face_recognition.py | 306 | def test_fd_command_line_interface(self): | CODE |
| LOW⚡ | tests/test_face_recognition.py | 316 | def test_fd_command_line_interface_folder(self): | CODE |
| LOW | tests/test_face_recognition.py | 328 | def test_fd_command_line_interface_hog_model(self): | CODE |
| LOW | tests/test_face_recognition.py | 337 | def test_fd_command_line_interface_cnn_model(self): | CODE |
| LOW | examples/face_recognition_knn.py | 153 | def show_prediction_labels_on_image(img_path, predictions): | CODE |
| LOW | examples/facerec_ipcamera_knn.py | 153 | def show_prediction_labels_on_image(frame, predictions): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/web_service_example.py | 35 | # Check if a valid image file was uploaded | COMMENT |
| LOW | examples/facerec_from_webcam.py | 70 | # Display the resulting image | COMMENT |
| LOW | examples/facerec_from_webcam_faster.py | 79 | # Display the results | COMMENT |
| LOW | examples/facerec_from_webcam_faster.py | 95 | # Display the resulting image | COMMENT |
| LOW | examples/face_recognition_knn.py | 180 | # Display the resulting image | COMMENT |
| LOW | examples/face_recognition_knn.py | 201 | # Print results on the console | COMMENT |
| LOW | examples/face_recognition_knn.py | 205 | # Display results overlaid on an image | COMMENT |
| LOW | examples/blur_faces_on_webcam.py | 26 | # Display the results | COMMENT |
| LOW | examples/blur_faces_on_webcam.py | 43 | # Display the resulting image | COMMENT |
| LOW | examples/identify_and_draw_boxes_on_faces.py | 69 | # Display the resulting image | COMMENT |
| LOW | examples/facerec_from_webcam_multiprocessing.py | 201 | # Display the resulting image | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | docs/conf.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | docs/conf.py | 61 | #source_encoding = 'utf-8-sig' | COMMENT |
| LOW | docs/conf.py | 81 | #language = None | COMMENT |
| LOW | docs/conf.py | 101 | # unit titles (such as .. function::). | COMMENT |
| LOW | docs/conf.py | 121 | # The theme to use for HTML and HTML Help pages. See the documentation for | COMMENT |
| LOW | docs/conf.py | 141 | # The name of an image file (relative to this directory) to place at the | COMMENT |
| LOW | docs/conf.py | 161 | # typographically correct entities. | COMMENT |
| LOW | docs/conf.py | 181 | #html_show_sourcelink = True | COMMENT |
| LOW | docs/conf.py | 221 | u'Face Recognition Documentation', | COMMENT |
| LOW | docs/conf.py | 281 | #texinfo_show_urls = 'footnote' | COMMENT |
| LOW | examples/web_service_example_Simplified_Chinese.py | 1 | # 这是一个非常简单的使用Web服务上传图片运行人脸识别的案例，后端服务器会识别这张图片是不是奥巴马，并把识别结果以json键值对输出 | COMMENT |
| LOW | examples/web_service_example.py | 1 | # This is a _very simple_ example of a web service that recognizes faces in uploaded images. | COMMENT |
| LOW | examples/find_faces_in_batches.py | 1 | import face_recognition | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | face_recognition/__init__.py | 7 | CODE | |
| LOW | face_recognition/__init__.py | 7 | CODE | |
| LOW | face_recognition/__init__.py | 7 | CODE | |
| LOW | face_recognition/__init__.py | 7 | CODE | |
| LOW | face_recognition/__init__.py | 7 | CODE | |
| LOW | face_recognition/__init__.py | 7 | CODE | |
| LOW | face_recognition/__init__.py | 7 | CODE | |
| LOW | face_recognition/face_detection_cli.py | 2 | CODE | |
| LOW | face_recognition/face_recognition_cli.py | 2 | CODE | |
| LOW | examples/blink_detection.py | 16 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/facerec_from_webcam.py | 51 | # If a match was found in known_face_encodings, just use the first one. | COMMENT |
| LOW | examples/facerec_from_webcam_faster.py | 63 | # # If a match was found in known_face_encodings, just use the first one. | COMMENT |
| LOW | examples/identify_and_draw_boxes_on_faces.py | 46 | # If a match was found in known_face_encodings, just use the first one. | COMMENT |
| LOW | examples/facerec_from_webcam_multiprocessing.py | 94 | # If a match was found in known_face_encodings, just use the first one. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/face_recognition_knn.py | 46 | CODE | |
| LOW | examples/facerec_ipcamera_knn.py | 51 | CODE | |
| LOW | examples/blink_detection.py | 21 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/facerec_on_raspberry_pi.py | 14 | # If this fails, make sure you have a camera connected to the RPi and that you | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | face_recognition/api.py | 10 | except Exception: | CODE |