| --- |
| license: mit |
| license_link: LICENSE |
| library_name: openvino |
| pipeline_tag: image-classification |
| tags: |
| - openvino |
| - intel |
| - face-detection |
| - face-reidentification |
| - edge-ai |
| - metro |
| - dlstreamer |
| language: |
| - en |
| --- |
| |
| # Facial Recognition |
|
|
| | Property | Value | |
| |---|---| |
| | **Category** | Face Detection + Re-Identification | |
| | **Base Model** | [face-detection-adas-0001](https://docs.openvino.ai/2024/omz_models_model_face_detection_adas_0001.html) + [face-reidentification-retail-0095](https://docs.openvino.ai/2024/omz_models_model_face_reidentification_retail_0095.html) (Open Model Zoo) | |
| | **Source Framework** | Caffe / PyTorch (Open Model Zoo) | |
| | **Supported Precisions** | FP32, FP16 | |
| | **Inference Engine** | OpenVINO | |
| | **Hardware** | CPU, GPU, NPU | |
| | **Detected Class(es)** | Human faces (detection) + 256-d face embeddings (re-identification) | |
|
|
| --- |
|
|
| ## Overview |
|
|
| Facial Recognition is a Metro Analytics use case that detects human faces in |
| images and video and computes a 256-dimensional embedding vector for each face, |
| enabling enrollment, search, and identification against a known gallery. |
|
|
| The pipeline composes two Intel Open Model Zoo models: |
|
|
| - **face-detection-adas-0001** -- an SSD-based face detector optimized for |
| automotive and surveillance cameras (FP16, 384x672 input). |
| - **face-reidentification-retail-0095** -- a compact CNN that maps a cropped |
| face to a 256-d embedding; cosine similarity between embeddings determines |
| identity. |
|
|
| These models are well-tested with OpenVINO Runtime and Intel DLStreamer's |
| `gvadetect` + `gvaclassify` pipeline. |
|
|
| Typical Metro deployments include: |
|
|
| - **Access Control** -- match employees or authorized personnel against an enrollment gallery. |
| - **VIP Identification** -- recognize known individuals in a crowd. |
| - **Search and Forensics** -- find a person of interest across multiple camera feeds. |
| - **Attendance Tracking** -- log when enrolled individuals enter or leave a facility. |
|
|
| > **Privacy Note:** Facial recognition involves biometric data. |
| > Ensure your deployment complies with applicable privacy regulations |
| > (GDPR, BIPA, etc.) and has proper consent mechanisms in place. |
|
|
| --- |
|
|
| ## Prerequisites |
|
|
| - Python 3.11+ |
| - [Install OpenVINO](https://docs.openvino.ai/2026/get-started/install-openvino.html) (latest version) |
| - [Install Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/get_started/install/install_guide_ubuntu.html) (latest version) |
|
|
| Create and activate a Python virtual environment before running the scripts: |
|
|
| ```bash |
| python3 -m venv .venv --system-site-packages |
| source .venv/bin/activate |
| ``` |
|
|
| > **Note:** The `--system-site-packages` flag is required so the virtual |
| > environment can access the system-installed OpenVINO and DLStreamer Python |
| > packages. |
|
|
| --- |
|
|
| ## Getting Started |
|
|
| ### Download Models |
|
|
| Run the provided script to download the face detection and re-identification |
| models from the Open Model Zoo: |
|
|
| ```bash |
| chmod +x export_and_quantize.sh |
| ./export_and_quantize.sh |
| ``` |
|
|
| The script performs the following steps: |
|
|
| 1. Installs `openvino`. |
| 2. Downloads `face-detection-adas-0001` (FP16) into `./intel/face-detection-adas-0001/FP16/`. |
| 3. Downloads `face-reidentification-retail-0095` (FP16) into `./intel/face-reidentification-retail-0095/FP16/`. |
| 4. Downloads a sample test video (`test_video.mp4`). |
|
|
| ### OpenVINO Sample |
|
|
| The sample below runs recognition on the sample video. It detects every face, |
| computes a 256-d embedding, and matches it against a gallery of previously seen |
| people. Each new person is enrolled and assigned a numeric ID; when the same |
| person is seen again, the gallery returns their existing ID. Every face is |
| annotated with its `ID <n>`, and the result is saved to `output_openvino.mp4`. |
| Change the `device` string to run on CPU, GPU, or NPU. |
|
|
| ```python |
| import cv2 |
| import numpy as np |
| import openvino as ov |
| |
| DETECTION_MODEL = "intel/face-detection-adas-0001/FP16/face-detection-adas-0001.xml" |
| REID_MODEL = "intel/face-reidentification-retail-0095/FP16/face-reidentification-retail-0095.xml" |
| INPUT_VIDEO = "test_video.mp4" |
| CONF_THRESHOLD = 0.6 |
| MATCH_THRESHOLD = 0.5 |
| |
| core = ov.Core() |
| |
| # Change device to "GPU" or "NPU" to run on integrated GPU or NPU. |
| det_model = core.compile_model(core.read_model(DETECTION_MODEL), "CPU") |
| reid_model = core.compile_model(core.read_model(REID_MODEL), "CPU") |
| |
| det_input = det_model.input(0) |
| det_h, det_w = det_input.shape[2], det_input.shape[3] |
| reid_input = reid_model.input(0) |
| reid_h, reid_w = reid_input.shape[2], reid_input.shape[3] |
| |
| |
| def detect_faces(img): |
| h0, w0 = img.shape[:2] |
| blob = cv2.resize(img, (det_w, det_h)) |
| blob = blob.transpose(2, 0, 1)[np.newaxis, ...].astype(np.float32) |
| detections = det_model([blob])[det_model.output(0)][0][0] |
| boxes = [] |
| for det in detections: |
| if float(det[2]) < CONF_THRESHOLD: |
| continue |
| x1 = max(0, int(det[3] * w0)) |
| y1 = max(0, int(det[4] * h0)) |
| x2 = min(w0, int(det[5] * w0)) |
| y2 = min(h0, int(det[6] * h0)) |
| if x2 > x1 and y2 > y1: |
| boxes.append((x1, y1, x2, y2)) |
| return boxes |
| |
| |
| def get_embedding(img, bbox): |
| x1, y1, x2, y2 = bbox |
| crop = img[y1:y2, x1:x2] |
| blob = cv2.resize(crop, (reid_w, reid_h)) |
| blob = blob.transpose(2, 0, 1)[np.newaxis, ...].astype(np.float32) |
| emb = reid_model([blob])[reid_model.output(0)].flatten() |
| return emb / np.linalg.norm(emb) |
| |
| |
| # Gallery of (numeric_id, embedding). recognize() returns an existing ID for a |
| # known face or enrolls a new one, keeping each person's ID stable over time. |
| gallery = [] |
| next_id = 1 |
| |
| |
| def recognize(embedding): |
| global next_id |
| best_index, best_sim = -1, 0.0 |
| for index, (_, gallery_emb) in enumerate(gallery): |
| sim = float(np.dot(embedding, gallery_emb)) |
| if sim > best_sim: |
| best_sim, best_index = sim, index |
| if best_sim >= MATCH_THRESHOLD: |
| person_id, gallery_emb = gallery[best_index] |
| # Blend the embedding into the gallery entry to stay robust to pose. |
| updated = 0.9 * gallery_emb + 0.1 * embedding |
| gallery[best_index] = (person_id, updated / np.linalg.norm(updated)) |
| return person_id |
| person_id = next_id |
| next_id += 1 |
| gallery.append((person_id, embedding)) |
| print(f"Enrolled ID {person_id}") |
| return person_id |
| |
| |
| cap = cv2.VideoCapture(INPUT_VIDEO) |
| fps = cap.get(cv2.CAP_PROP_FPS) or 12 |
| frame_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) |
| frame_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
| writer = cv2.VideoWriter( |
| "output_openvino.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (frame_w, frame_h)) |
| |
| while True: |
| ok, frame = cap.read() |
| if not ok: |
| break |
| for bbox in detect_faces(frame): |
| person_id = recognize(get_embedding(frame, bbox)) |
| x1, y1, x2, y2 = bbox |
| cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) |
| cv2.putText(frame, f"ID {person_id}", (x1, max(15, y1 - 8)), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) |
| writer.write(frame) |
| |
| cap.release() |
| writer.release() |
| print(f"Total identities recognized: {len(gallery)}") |
| print("Saved: output_openvino.mp4") |
| ``` |
|
|
| **Device targets:** |
|
|
| - `"CPU"` -- default, works on all Intel platforms. |
| - `"GPU"` -- Intel integrated or discrete GPU. |
| - `"NPU"` -- Intel NPU; face-detection-adas-0001 FP16 is NPU-compatible. |
|
|
| #### Expected Output |
|
|
|  |
|
|
| ### DLStreamer Sample |
|
|
| The pipeline below runs the face detector via `gvadetect` and the |
| re-identification model via `gvaclassify` on the video. Frames are pulled through |
| an `appsink`, where each face's embedding is matched against a gallery to assign |
| a stable numeric ID (new people are enrolled, returning people keep their ID). |
| Every face is annotated with its `ID <n>` and the result is saved to |
| `output_dlstreamer.mp4`. |
|
|
| > **Notes on running this sample:** |
| > |
| > - Export `PYTHONPATH` so the DLStreamer Python modules (`gi`, `gstgva`) are |
| > importable: |
| > |
| > ```bash |
| > source /opt/intel/openvino_2026/setupvars.sh |
| > source /opt/intel/dlstreamer/scripts/setup_dls_env.sh |
| > export PYTHONPATH=/opt/intel/dlstreamer/python:\ |
| > /opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-} |
| > ``` |
| > |
| > - The re-identification embedding is attached as a tensor on each face's |
| > region-of-interest metadata. Convert the stream to `BGR` **before** |
| > `gvadetect`/`gvaclassify` so a downstream format conversion does not strip |
| > those tensors before the `appsink` reads them. |
| |
| ```python |
| import gi |
| |
| gi.require_version("Gst", "1.0") |
| from gi.repository import Gst |
|
|
| Gst.init([]) |
|
|
| import numpy as np |
| import cv2 |
| from gstgva import VideoFrame |
|
|
| INPUT_VIDEO = "test_video.mp4" |
| OUTPUT_VIDEO = "output_dlstreamer.mp4" |
| DETECTION_MODEL = "intel/face-detection-adas-0001/FP16/face-detection-adas-0001.xml" |
| REID_MODEL = "intel/face-reidentification-retail-0095/FP16/face-reidentification-retail-0095.xml" |
| # For CPU: change "GPU" to "CPU". For NPU: change "GPU" to "NPU". |
| DEVICE = "GPU" |
| DET_THRESHOLD = 0.6 |
| MATCH_THRESHOLD = 0.5 |
|
|
| # Gallery of (numeric_id, embedding). recognize() returns an existing ID for a |
| # known face or enrolls a new one, keeping each person's ID stable over time. |
| gallery = [] |
| next_id = 1 |
|
|
|
|
| def recognize(embedding): |
| global next_id |
| best_index, best_sim = -1, 0.0 |
| for index, (_, gallery_emb) in enumerate(gallery): |
| sim = float(np.dot(embedding, gallery_emb)) |
| if sim > best_sim: |
| best_sim, best_index = sim, index |
| if best_sim >= MATCH_THRESHOLD: |
| person_id, gallery_emb = gallery[best_index] |
| # Blend the embedding into the gallery entry to stay robust to pose. |
| updated = 0.9 * gallery_emb + 0.1 * embedding |
| gallery[best_index] = (person_id, updated / np.linalg.norm(updated)) |
| return person_id |
| person_id = next_id |
| next_id += 1 |
| gallery.append((person_id, embedding)) |
| print(f"Enrolled ID {person_id}", flush=True) |
| return person_id |
| |
|
|
| def face_embeddings(video_frame): |
| """Yield ((x, y, w, h), normalized_embedding) for each classified face.""" |
| for region in video_frame.regions(): |
| rect = region.rect() |
| emb = None |
| for tensor in region.tensors(): |
| if tensor.is_detection(): |
| continue |
| data = np.array(tensor.data(), dtype=np.float32) |
| if data.size >= 256: |
| emb = data[:256] |
| if emb is None: |
| continue |
| emb = emb / (np.linalg.norm(emb) + 1e-9) |
| yield (int(rect.x), int(rect.y), int(rect.w), int(rect.h)), emb |
| |
|
|
| # Convert to BGR before inference so gvaclassify's embedding tensors survive to |
| # the appsink (a later format-changing videoconvert would strip them). |
| pipeline = Gst.parse_launch( |
| f"filesrc location={INPUT_VIDEO} ! decodebin3 ! " |
| "videoconvert ! video/x-raw,format=BGR ! " |
| f"gvadetect model={DETECTION_MODEL} device={DEVICE} " |
| f"threshold={DET_THRESHOLD} ! queue ! " |
| f"gvaclassify model={REID_MODEL} device={DEVICE} ! queue ! " |
| "appsink name=sink emit-signals=true sync=false max-buffers=4 drop=false" |
| ) |
| sink = pipeline.get_by_name("sink") |
| |
| writer = {"w": None} |
|
|
|
|
| def on_video(sink): |
| sample = sink.emit("pull-sample") |
| if sample is None: |
| return Gst.FlowReturn.OK |
| vf = VideoFrame(sample.get_buffer(), caps=sample.get_caps()) |
| labeled = [] |
| for (x, y, w, h), emb in face_embeddings(vf): |
| labeled.append((x, y, w, h, recognize(emb))) |
| |
| with vf.data() as mat: |
| frame = mat.copy() |
| |
| if writer["w"] is None: |
| frame_h, frame_w = frame.shape[:2] |
| structure = sample.get_caps().get_structure(0) |
| ok_fr, fps_n, fps_d = structure.get_fraction("framerate") |
| fps = fps_n / fps_d if ok_fr and fps_d else 12 |
| writer["w"] = cv2.VideoWriter( |
| OUTPUT_VIDEO, cv2.VideoWriter_fourcc(*"mp4v"), fps, (frame_w, frame_h)) |
| |
| for x, y, w, h, person_id in labeled: |
| cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) |
| cv2.putText(frame, f"ID {person_id}", (x, max(15, y - 8)), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) |
| writer["w"].write(frame) |
| return Gst.FlowReturn.OK |
| |
|
|
| sink.connect("new-sample", on_video) |
| pipeline.set_state(Gst.State.PLAYING) |
| pipeline.get_bus().timed_pop_filtered( |
| Gst.CLOCK_TIME_NONE, Gst.MessageType.EOS | Gst.MessageType.ERROR) |
| pipeline.set_state(Gst.State.NULL) |
| if writer["w"] is not None: |
| writer["w"].release() |
| print(f"Total identities recognized: {len(gallery)}", flush=True) |
| print(f"Saved: {OUTPUT_VIDEO}", flush=True) |
| ``` |
| |
| **Device targets:** |
|
|
| - `DEVICE = "GPU"` -- default in the sample code. |
| - `DEVICE = "CPU"` -- change `"GPU"` to `"CPU"`. |
| - `DEVICE = "NPU"` -- change `"GPU"` to `"NPU"`; use `batch-size=1` and `nireq=4` for best NPU utilization. |
|
|
| #### Expected Output |
|
|
|  |
|
|
| --- |
|
|
| ## License |
|
|
| Licensed under the MIT License. See [LICENSE](LICENSE) for details. |
|
|
| ## References |
|
|
| - [face-detection-adas-0001](https://docs.openvino.ai/2024/omz_models_model_face_detection_adas_0001.html) |
| - [face-reidentification-retail-0095](https://docs.openvino.ai/2024/omz_models_model_face_reidentification_retail_0095.html) |
| - [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) |
| - [OpenVINO Documentation](https://docs.openvino.ai/) |
| - [Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/index.html) |
|
|
