--- license: mit license_link: LICENSE library_name: openvino pipeline_tag: image-classification tags: - openvino - intel - face-detection - face-matching - face-reidentification - edge-ai - metro - dlstreamer language: - en --- # Face Matching | Property | Value | |---|---| | **Category** | Face Detection + One-to-One Verification | | **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 (matching) | --- ## Overview Face Matching is a Metro Analytics use case that verifies a specific identity: given a reference face image, it locates that person inside a scene that may contain several people and highlights only the matching face. Each detected face is compared to the reference by cosine similarity of its embedding vector. It uses the same two-stage pipeline as [facial-recognition](../facial-recognition/): - **face-detection-adas-0001** -- detects every face in the scene. - **face-reidentification-retail-0095** -- computes a 256-d embedding per face. The difference from full facial recognition is scope: face matching verifies a single reference identity and highlights only that person, without maintaining a gallery database. Typical Metro deployments include: - **Badge Verification** -- compare a live face to a badge photo at entry gates. - **Document Verification** -- match a passport or ID photo to the holder. - **Duplicate Detection** -- check if two records belong to the same person. - **Re-identification Confirmation** -- confirm a person flagged by the search system. > **Privacy Note:** Face matching 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 downloads `face-detection-adas-0001` and `face-reidentification-retail-0095` in FP16, downloads the sample video, and captures a reference face image (`face_a.jpg`) of the left subject from it. ### OpenVINO Sample The sample below verifies identity inside a scene: it loads the captured reference image of the subject (`face_a.jpg`, the woman who pauses on the left of the sample video), computes its embedding, then reads a frame from the video that contains several people. It detects every face in the frame, embeds each one, and draws a green box only on the face whose similarity to the reference is highest and above the match threshold. 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" REFERENCE_IMAGE = "face_a.jpg" # reference: left subject captured from the video SCENE_VIDEO = "test_video.mp4" # scene containing the reference subject plus others SCENE_FRAME = 480 # frame index of the two-person scene (~40s) CONF_THRESHOLD = 0.5 MATCH_THRESHOLD = 0.5 core = ov.Core() # Change device to "GPU" or "NPU" to run on integrated GPU or NPU. det_compiled = core.compile_model(core.read_model(DETECTION_MODEL), "CPU") reid_compiled = core.compile_model(core.read_model(REID_MODEL), "CPU") det_input = det_compiled.input(0) det_h, det_w = det_input.shape[2], det_input.shape[3] reid_input = reid_compiled.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) dets = det_compiled([blob])[det_compiled.output(0)][0][0] faces = [] for d in dets: if float(d[2]) < CONF_THRESHOLD: continue x1 = max(0, int(d[3] * w0)) y1 = max(0, int(d[4] * h0)) x2 = min(w0, int(d[5] * w0)) y2 = min(h0, int(d[6] * h0)) if x2 > x1 and y2 > y1: faces.append((x1, y1, x2, y2)) return faces 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_compiled([blob])[reid_compiled.output(0)].flatten() return emb / np.linalg.norm(emb) # 1. Embed the reference face. reference = cv2.imread(REFERENCE_IMAGE) ref_faces = detect_faces(reference) if not ref_faces: raise SystemExit("No face detected in the reference image") ref_bbox = max(ref_faces, key=lambda b: (b[2] - b[0]) * (b[3] - b[1])) ref_emb = get_embedding(reference, ref_bbox) # 2. Read a scene frame that contains several people. cap = cv2.VideoCapture(SCENE_VIDEO) cap.set(cv2.CAP_PROP_POS_FRAMES, SCENE_FRAME) ok, frame = cap.read() cap.release() if not ok: raise SystemExit(f"Could not read frame {SCENE_FRAME} from {SCENE_VIDEO}") # 3. Compare every face in the scene to the reference; keep the best match. best_bbox = None best_sim = 0.0 for bbox in detect_faces(frame): sim = float(np.dot(get_embedding(frame, bbox), ref_emb)) if sim > best_sim: best_sim = sim best_bbox = bbox if best_bbox is not None and best_sim >= MATCH_THRESHOLD: x1, y1, x2, y2 = best_bbox cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.putText(frame, f"MATCH {best_sim:.2f}", (x1, max(15, y1 - 8)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) print(f"Matched reference identity, similarity {best_sim:.4f}") else: print(f"No matching face found (best similarity {best_sim:.4f})") cv2.imwrite("output_openvino.jpg", frame) print("Saved: output_openvino.jpg") ``` **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 ![OpenVINO expected output](expected_output_openvino.jpg) ### 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 detected face's embedding is compared to the reference embedding computed from `face_a.jpg`. Only faces that match the reference are boxed, so the annotated `output_dlstreamer.mp4` highlights just the reference subject even when other people are present. > **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" REFERENCE_IMAGE = "face_a.jpg" 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 = 0.4 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 def run_pipeline(source_desc, on_frame): # 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"{source_desc} ! 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") sink.connect("new-sample", on_frame) 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) # 1. Compute the reference embedding from the reference image. ref = {"emb": None, "area": 0} def on_reference(sink): sample = sink.emit("pull-sample") if sample is None: return Gst.FlowReturn.OK vf = VideoFrame(sample.get_buffer(), caps=sample.get_caps()) for (x, y, w, h), emb in face_embeddings(vf): if w * h > ref["area"]: ref["area"] = w * h ref["emb"] = emb return Gst.FlowReturn.OK run_pipeline(f"filesrc location={REFERENCE_IMAGE} ! jpegdec", on_reference) if ref["emb"] is None: raise SystemExit("No face detected in the reference image") ref_emb = ref["emb"] # 2. Process the video, boxing only faces that match the reference identity. writer = {"w": None} match_frames = 0 def on_video(sink): global match_frames sample = sink.emit("pull-sample") if sample is None: return Gst.FlowReturn.OK vf = VideoFrame(sample.get_buffer(), caps=sample.get_caps()) matches = [] for (x, y, w, h), emb in face_embeddings(vf): similarity = float(np.dot(emb, ref_emb)) if similarity >= MATCH_THRESHOLD: matches.append((x, y, w, h, similarity)) 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, similarity in matches: cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(frame, f"MATCH {similarity:.2f}", (x, max(15, y - 8)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) if matches: match_frames += 1 writer["w"].write(frame) return Gst.FlowReturn.OK run_pipeline(f"filesrc location={INPUT_VIDEO} ! decodebin3", on_video) if writer["w"] is not None: writer["w"].release() print(f"Frames with a matched face: {match_frames}", 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 ![DLStreamer expected output](expected_output_dlstreamer.gif) --- ## 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)