Refactor face library page (#17424)

* Section faces by event id

* Make score keeping more robust

* layout improvements

* Cleanup dialog

* Fix clicking behavior

* Add view in explore option

* math.round

* Don't require events

* Cleanup

* Remove selection

* Don't require

* Change dialog size with snapshot

* Use filename as key

* fix key

* Rework layout for mobile

* Handle mobile landscape

* Fix train issue

* Match logic

* Move deletion logic

* Fix reprocessing

* Support creating a new face

* Translations

* Do sorting in frontend

* Adjust unknown

* Cleanup

* Set max limit to faces to recognize

* Fix sorting

* Fix

docs/docs/configuration/face_recognition.md

@@ -59,7 +59,7 @@ Fine-tune face recognition with these optional parameters:
 ### Recognition
 
 - `model_size`: Which model size to use, options are `small` or `large`
-- `unknown_score`: Min score to mark a person as a potential match, matches below this will be marked as unknown.
+- `unknown_score`: Min score to mark a person as a potential match, matches at or below this will be marked as unknown.
   - Default: `0.8`.
 - `recognition_threshold`: Recognition confidence score required to add the face to the object as a sub label.
   - Default: `0.9`.

frigate/api/classification.py

@@ -41,13 +41,9 @@ def get_faces():
 
         face_dict[name] = []
 
-        for file in sorted(
+        for file in filter(
-            filter(
+            lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
-                lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
+            os.listdir(face_dir),
-                os.listdir(face_dir),
-            ),
-            key=lambda f: os.path.getctime(os.path.join(face_dir, f)),
-            reverse=True,
         ):
             face_dict[name].append(file)
 
@@ -125,10 +121,13 @@ def train_face(request: Request, name: str, body: dict = None):
     sanitized_name = sanitize_filename(name)
     rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
     new_name = f"{sanitized_name}-{rand_id}.webp"
-    new_file = os.path.join(FACE_DIR, f"{sanitized_name}/{new_name}")
+    new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
+
+    if not os.path.exists(new_file_folder):
+        os.mkdir(new_file_folder)
 
     if training_file_name:
-        shutil.move(training_file, new_file)
+        shutil.move(training_file, os.path.join(new_file_folder, new_name))
     else:
         try:
             event: Event = Event.get(Event.id == event_id)
@@ -155,7 +154,7 @@ def train_face(request: Request, name: str, body: dict = None):
         x2 = x1 + int(face_box[2] * detect_config.width) - 4
         y2 = y1 + int(face_box[3] * detect_config.height) - 4
         face = snapshot[y1:y2, x1:x2]
-        cv2.imwrite(new_file, face)
+        cv2.imwrite(os.path.join(new_file_folder, new_name), face)
 
     context: EmbeddingsContext = request.app.embeddings
     context.clear_face_classifier()

frigate/data_processing/real_time/face.py

@@ -33,7 +33,8 @@ logger = logging.getLogger(__name__)
 
 
 MAX_DETECTION_HEIGHT = 1080
-MIN_MATCHING_FACES = 2
+MAX_FACES_ATTEMPTS_AFTER_REC = 6
+MAX_FACE_ATTEMPTS = 12
 
 
 class FaceRealTimeProcessor(RealTimeProcessorApi):
@@ -170,6 +171,23 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             )
             return
 
+        # check if we have hit limits
+        if (
+            id in self.person_face_history
+            and len(self.person_face_history[id]) >= MAX_FACES_ATTEMPTS_AFTER_REC
+        ):
+            # if we are at max attempts after rec and we have a rec
+            if obj_data.get("sub_label"):
+                logger.debug(
+                    "Not processing due to hitting max attempts after true recognition."
+                )
+                return
+
+            # if we don't have a rec and are at max attempts
+            if len(self.person_face_history[id]) >= MAX_FACE_ATTEMPTS:
+                logger.debug("Not processing due to hitting max rec attempts.")
+                return
+
         face: Optional[dict[str, any]] = None
 
         if self.requires_face_detection:
@@ -241,7 +259,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
         sub_label, score = res
 
-        if score < self.face_config.unknown_score:
+        if score <= self.face_config.unknown_score:
             sub_label = "unknown"
 
         logger.debug(
@@ -255,13 +273,23 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             os.makedirs(folder, exist_ok=True)
             cv2.imwrite(file, face_frame)
 
+            files = sorted(
+                filter(lambda f: (f.endswith(".webp")), os.listdir(folder)),
+                key=lambda f: os.path.getctime(os.path.join(folder, f)),
+                reverse=True,
+            )
+
+            # delete oldest face image if maximum is reached
+            if len(files) > self.config.face_recognition.save_attempts:
+                os.unlink(os.path.join(folder, files[-1]))
+
         if id not in self.person_face_history:
             self.person_face_history[id] = []
 
         self.person_face_history[id].append(
             (sub_label, score, face_frame.shape[0] * face_frame.shape[1])
         )
-        (weighted_sub_label, weighted_score) = self.weighted_average_by_area(
+        (weighted_sub_label, weighted_score) = self.weighted_average(
             self.person_face_history[id]
         )
 
@@ -297,6 +325,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
             sub_label, score = res
 
+            if score <= self.face_config.unknown_score:
+                sub_label = "unknown"
+
             return {"success": True, "score": score, "face_name": sub_label}
         elif topic == EmbeddingsRequestEnum.register_face.value:
             rand_id = "".join(
@@ -366,6 +397,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
             sub_label, score = res
 
+            if score <= self.face_config.unknown_score:
+                sub_label = "unknown"
+
             if self.config.face_recognition.save_attempts:
                 # write face to library
                 folder = os.path.join(FACE_DIR, "train")
@@ -375,38 +409,49 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 )
                 shutil.move(current_file, new_file)
 
-                files = sorted(
-                    filter(lambda f: (f.endswith(".webp")), os.listdir(folder)),
-                    key=lambda f: os.path.getctime(os.path.join(folder, f)),
-                    reverse=True,
-                )
-
-                # delete oldest face image if maximum is reached
-                if len(files) > self.config.face_recognition.save_attempts:
-                    os.unlink(os.path.join(folder, files[-1]))
-
     def expire_object(self, object_id: str):
         if object_id in self.person_face_history:
             self.person_face_history.pop(object_id)
 
-    def weighted_average_by_area(self, results_list: list[tuple[str, float, int]]):
+    def weighted_average(
-        score_count = {}
+        self, results_list: list[tuple[str, float, int]], max_weight: int = 4000
+    ):
+        """
+        Calculates a robust weighted average, capping the area weight and giving more weight to higher scores.
+
+        Args:
+            results_list: A list of tuples, where each tuple contains (name, score, face_area).
+            max_weight: The maximum weight to apply based on face area.
+
+        Returns:
+            A tuple containing the prominent name and its weighted average score, or (None, 0.0) if the list is empty.
+        """
+        if not results_list:
+            return None, 0.0
+
         weighted_scores = {}
-        total_face_areas = {}
+        total_weights = {}
 
         for name, score, face_area in results_list:
+            if name == "unknown":
+                continue
+
             if name not in weighted_scores:
-                score_count[name] = 1
                 weighted_scores[name] = 0.0
-                total_face_areas[name] = 0.0
+                total_weights[name] = 0.0
-            else:
-                score_count[name] += 1
 
-            weighted_scores[name] += score * face_area
+            # Capped weight based on face area
-            total_face_areas[name] += face_area
+            weight = min(face_area, max_weight)
 
-        prominent_name = max(score_count)
+            # Score-based weighting (higher scores get more weight)
+            weight *= (score - self.face_config.unknown_score) * 10
+            weighted_scores[name] += score * weight
+            total_weights[name] += weight
 
-        return prominent_name, weighted_scores[prominent_name] / total_face_areas[
+        if not weighted_scores:
-            prominent_name
+            return None, 0.0
-        ]
+
+        best_name = max(weighted_scores, key=weighted_scores.get)
+        weighted_average = weighted_scores[best_name] / total_weights[best_name]
+
+        return best_name, weighted_average

web/public/locales/en/views/faceLibrary.json

@@ -17,6 +17,7 @@
   "createFaceLibrary": {
     "title": "Create Face Library",
     "desc": "Create a new face library",
+    "new": "Create New Face",
     "nextSteps": "It is recommended to use the Train tab to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle."
   },
   "train": {

web/src/pages/FaceLibrary.tsx

@@ -3,6 +3,7 @@ import TimeAgo from "@/components/dynamic/TimeAgo";
 import AddFaceIcon from "@/components/icons/AddFaceIcon";
 import ActivityIndicator from "@/components/indicators/activity-indicator";
 import CreateFaceWizardDialog from "@/components/overlay/detail/FaceCreateWizardDialog";
+import TextEntryDialog from "@/components/overlay/dialog/TextEntryDialog";
 import UploadImageDialog from "@/components/overlay/dialog/UploadImageDialog";
 import { Button } from "@/components/ui/button";
 import {
@@ -32,13 +33,23 @@ import { useFormattedTimestamp } from "@/hooks/use-date-utils";
 import useKeyboardListener from "@/hooks/use-keyboard-listener";
 import useOptimisticState from "@/hooks/use-optimistic-state";
 import { cn } from "@/lib/utils";
+import { Event } from "@/types/event";
 import { FaceLibraryData, RecognizedFaceData } from "@/types/face";
 import { FaceRecognitionConfig, FrigateConfig } from "@/types/frigateConfig";
+import { TooltipPortal } from "@radix-ui/react-tooltip";
 import axios from "axios";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
 import { isDesktop, isMobile } from "react-device-detect";
 import { useTranslation } from "react-i18next";
-import { LuImagePlus, LuRefreshCw, LuScanFace, LuTrash2 } from "react-icons/lu";
+import {
+  LuImagePlus,
+  LuPlus,
+  LuRefreshCw,
+  LuScanFace,
+  LuSearch,
+  LuTrash2,
+} from "react-icons/lu";
+import { useNavigate } from "react-router-dom";
 import { toast } from "sonner";
 import useSWR from "swr";
 
@@ -391,14 +402,53 @@ function TrainingGrid({
   onClickFace,
   onRefresh,
 }: TrainingGridProps) {
-  const { t } = useTranslation(["views/faceLibrary"]);
+  const { t } = useTranslation(["views/faceLibrary", "views/explore"]);
+  const navigate = useNavigate();
 
   // face data
 
-  const [selectedEvent, setSelectedEvent] = useState<RecognizedFaceData>();
+  const faceGroups = useMemo(() => {
+    const groups: { [eventId: string]: RecognizedFaceData[] } = {};
+
+    Array.from(new Set(attemptImages))
+      .sort()
+      .reverse()
+      .forEach((image) => {
+        const parts = image.split("-");
+        const data = {
+          filename: image,
+          timestamp: Number.parseFloat(parts[0]),
+          eventId: `${parts[0]}-${parts[1]}`,
+          name: parts[2],
+          score: Number.parseFloat(parts[3]),
+        };
+
+        if (groups[data.eventId]) {
+          groups[data.eventId].push(data);
+        } else {
+          groups[data.eventId] = [data];
+        }
+      });
+
+    return groups;
+  }, [attemptImages]);
+
+  const eventIdsQuery = useMemo(
+    () => Object.keys(faceGroups).join(","),
+    [faceGroups],
+  );
+
+  const { data: events } = useSWR<Event[]>([
+    "event_ids",
+    { ids: eventIdsQuery },
+  ]);
+
+  // selection
+
+  const [selectedEvent, setSelectedEvent] = useState<Event>();
 
   const formattedDate = useFormattedTimestamp(
-    selectedEvent?.timestamp ?? 0,
+    selectedEvent?.start_time ?? 0,
     config?.ui.time_format == "24hour"
       ? t("time.formattedTimestampWithYear.24hour", { ns: "common" })
       : t("time.formattedTimestampWithYear.12hour", { ns: "common" }),
@@ -415,23 +465,32 @@ function TrainingGrid({
           }
         }}
       >
-        <DialogContent>
+        <DialogContent
+          className={cn(
+            "",
+            selectedEvent?.has_snapshot && isDesktop && "max-w-7xl",
+          )}
+        >
           <DialogHeader>
             <DialogTitle>{t("details.face")}</DialogTitle>
             <DialogDescription>{t("details.faceDesc")}</DialogDescription>
           </DialogHeader>
           <div className="flex flex-col gap-1.5">
             <div className="text-sm text-primary/40">{t("details.person")}</div>
-            <div className="text-sm capitalize">{selectedEvent?.name}</div>
-          </div>
-          <div className="flex flex-col gap-1.5">
-            <div className="text-sm text-primary/40">
-              {t("details.confidence")}
-            </div>
             <div className="text-sm capitalize">
-              {(selectedEvent?.score || 0) * 100}%
+              {selectedEvent?.sub_label ?? "Unknown"}
             </div>
           </div>
+          {selectedEvent?.data.sub_label_score && (
+            <div className="flex flex-col gap-1.5">
+              <div className="text-sm text-primary/40">
+                {t("details.confidence")}
+              </div>
+              <div className="text-sm capitalize">
+                {Math.round(selectedEvent?.data?.sub_label_score || 0) * 100}%
+              </div>
+            </div>
+          )}
           <div className="flex flex-col gap-1.5">
             <div className="text-sm text-primary/40">
               {t("details.timestamp")}
@@ -440,36 +499,89 @@ function TrainingGrid({
           </div>
           <img
             className="w-full"
-            src={`${baseUrl}api/events/${selectedEvent?.eventId}/thumbnail.jpg`}
+            src={`${baseUrl}api/events/${selectedEvent?.id}/${selectedEvent?.has_snapshot ? "snapshot.jpg" : "thumbnail.jpg"}`}
           />
         </DialogContent>
       </Dialog>
 
       <div className="scrollbar-container flex flex-wrap gap-2 overflow-y-scroll p-1">
-        {attemptImages.map((image: string) => (
+        {Object.entries(faceGroups).map(([key, group]) => {
-          <FaceAttempt
+          const event = events?.find((ev) => ev.id == key);
-            key={image}
+
-            image={image}
+          return (
-            faceNames={faceNames}
+            <div
-            recognitionConfig={config.face_recognition}
+              key={key}
-            selected={selectedFaces.includes(image)}
+              className={cn(
-            onClick={(data, meta) => {
+                "flex flex-col gap-2 rounded-lg bg-card p-2",
-              if (meta) {
+                isMobile && "w-full",
-                onClickFace(image, meta);
+              )}
-              } else {
+            >
-                setSelectedEvent(data);
+              <div className="flex flex-row justify-between">
-              }
+                <div className="capitalize">
-            }}
+                  Person
-            onRefresh={onRefresh}
+                  {event?.sub_label
-          />
+                    ? `: ${event.sub_label} (${Math.round((event.data.sub_label_score || 0) * 100)}%)`
-        ))}
+                    : ": Unknown"}
+                </div>
+                {event && (
+                  <Tooltip>
+                    <TooltipTrigger>
+                      <div
+                        className="cursor-pointer"
+                        onClick={() => {
+                          navigate(`/explore?event_id=${event.id}`);
+                        }}
+                      >
+                        <LuSearch className="size-4 text-muted-foreground" />
+                      </div>
+                    </TooltipTrigger>
+                    <TooltipPortal>
+                      <TooltipContent>
+                        {t("details.item.button.viewInExplore", {
+                          ns: "views/explore",
+                        })}
+                      </TooltipContent>
+                    </TooltipPortal>
+                  </Tooltip>
+                )}
+              </div>
+
+              <div
+                className={cn(
+                  "gap-2",
+                  isDesktop
+                    ? "flex flex-row flex-wrap"
+                    : "grid grid-cols-2 sm:grid-cols-5 lg:grid-cols-6",
+                )}
+              >
+                {group.map((data: RecognizedFaceData) => (
+                  <FaceAttempt
+                    key={data.filename}
+                    data={data}
+                    faceNames={faceNames}
+                    recognitionConfig={config.face_recognition}
+                    selected={selectedFaces.includes(data.filename)}
+                    onClick={(data, meta) => {
+                      if (meta || selectedFaces.length > 0) {
+                        onClickFace(data.filename, true);
+                      } else if (event) {
+                        setSelectedEvent(event);
+                      }
+                    }}
+                    onRefresh={onRefresh}
+                  />
+                ))}
+              </div>
+            </div>
+          );
+        })}
       </div>
     </>
   );
 }
 
 type FaceAttemptProps = {
-  image: string;
+  data: RecognizedFaceData;
   faceNames: string[];
   recognitionConfig: FaceRecognitionConfig;
   selected: boolean;
@@ -477,7 +589,7 @@ type FaceAttemptProps = {
   onRefresh: () => void;
 };
 function FaceAttempt({
-  image,
+  data,
   faceNames,
   recognitionConfig,
   selected,
@@ -485,16 +597,6 @@ function FaceAttempt({
   onRefresh,
 }: FaceAttemptProps) {
   const { t } = useTranslation(["views/faceLibrary"]);
-  const data = useMemo<RecognizedFaceData>(() => {
-    const parts = image.split("-");
-
-    return {
-      timestamp: Number.parseFloat(parts[0]),
-      eventId: `${parts[0]}-${parts[1]}`,
-      name: parts[2],
-      score: Number.parseFloat(parts[3]),
-    };
-  }, [image]);
 
   const scoreStatus = useMemo(() => {
     if (data.score >= recognitionConfig.recognition_threshold) {
@@ -508,6 +610,8 @@ function FaceAttempt({
 
   // interaction
 
+  const [newFace, setNewFace] = useState(false);
+
   const imgRef = useRef<HTMLImageElement | null>(null);
 
   useContextMenu(imgRef, () => {
@@ -519,7 +623,9 @@ function FaceAttempt({
   const onTrainAttempt = useCallback(
     (trainName: string) => {
       axios
-        .post(`/faces/train/${trainName}/classify`, { training_file: image })
+        .post(`/faces/train/${trainName}/classify`, {
+          training_file: data.filename,
+        })
         .then((resp) => {
           if (resp.status == 200) {
             toast.success(t("toast.success.trainedFace"), {
@@ -538,12 +644,12 @@ function FaceAttempt({
           });
         });
     },
-    [image, onRefresh, t],
+    [data, onRefresh, t],
   );
 
   const onReprocess = useCallback(() => {
     axios
-      .post(`/faces/reprocess`, { training_file: image })
+      .post(`/faces/reprocess`, { training_file: data.filename })
       .then((resp) => {
         if (resp.status == 200) {
           toast.success(t("toast.success.updatedFaceScore"), {
@@ -561,79 +667,102 @@ function FaceAttempt({
           position: "top-center",
         });
       });
-  }, [image, onRefresh, t]);
+  }, [data, onRefresh, t]);
 
   return (
-    <div
+    <>
-      className={cn(
+      {newFace && (
-        "relative flex cursor-pointer flex-col rounded-lg outline outline-[3px]",
+        <TextEntryDialog
-        selected
+          open={true}
-          ? "shadow-selected outline-selected"
+          setOpen={setNewFace}
-          : "outline-transparent duration-500",
+          title={t("createFaceLibrary.new")}
-      )}
+          onSave={(newName) => onTrainAttempt(newName)}
-    >
-      <div className="relative w-full overflow-hidden rounded-t-lg border border-t-0 *:text-card-foreground">
-        <img
-          ref={imgRef}
-          className="size-44"
-          src={`${baseUrl}clips/faces/train/${image}`}
-          onClick={(e) => onClick(data, e.metaKey || e.ctrlKey)}
         />
-        <div className="absolute bottom-1 right-1 z-10 rounded-lg bg-black/50 px-2 py-1 text-xs text-white">
+      )}
-          <TimeAgo className="text-white" time={data.timestamp * 1000} dense />
+
+      <div
+        className={cn(
+          "relative flex cursor-pointer flex-col rounded-lg outline outline-[3px]",
+          selected
+            ? "shadow-selected outline-selected"
+            : "outline-transparent duration-500",
+        )}
+      >
+        <div className="relative w-full overflow-hidden rounded-lg *:text-card-foreground">
+          <img
+            ref={imgRef}
+            className={cn("size-44", isMobile && "w-full")}
+            src={`${baseUrl}clips/faces/train/${data.filename}`}
+            onClick={(e) => onClick(data, e.metaKey || e.ctrlKey)}
+          />
+          <div className="absolute bottom-1 right-1 z-10 rounded-lg bg-black/50 px-2 py-1 text-xs text-white">
+            <TimeAgo
+              className="text-white"
+              time={data.timestamp * 1000}
+              dense
+            />
+          </div>
         </div>
-      </div>
+        <div className="p-2">
-      <div className="rounded-b-lg bg-card p-2">
+          <div className="flex w-full flex-row items-center justify-between gap-2">
-        <div className="flex w-full flex-row items-center justify-between gap-2">
+            <div className="flex flex-col items-start text-xs text-primary-variant">
-          <div className="flex flex-col items-start text-xs text-primary-variant">
+              <div className="capitalize">{data.name}</div>
-            <div className="capitalize">{data.name}</div>
+              <div
-            <div
+                className={cn(
-              className={cn(
+                  "",
-                "",
+                  scoreStatus == "match" && "text-success",
-                scoreStatus == "match" && "text-success",
+                  scoreStatus == "potential" && "text-orange-400",
-                scoreStatus == "potential" && "text-orange-400",
+                  scoreStatus == "unknown" && "text-danger",
-                scoreStatus == "unknown" && "text-danger",
+                )}
-              )}
+              >
-            >
+                {Math.round(data.score * 100)}%
-              {Math.round(data.score * 100)}%
+              </div>
+            </div>
+            <div className="flex flex-row items-start justify-end gap-5 md:gap-4">
+              <Tooltip>
+                <DropdownMenu>
+                  <DropdownMenuTrigger asChild>
+                    <TooltipTrigger>
+                      <AddFaceIcon className="size-5 cursor-pointer text-primary-variant hover:text-primary" />
+                    </TooltipTrigger>
+                  </DropdownMenuTrigger>
+                  <DropdownMenuContent>
+                    <DropdownMenuLabel>{t("trainFaceAs")}</DropdownMenuLabel>
+                    <DropdownMenuItem
+                      className="flex cursor-pointer gap-2 capitalize"
+                      onClick={() => setNewFace(true)}
+                    >
+                      <LuPlus />
+                      {t("createFaceLibrary.new")}
+                    </DropdownMenuItem>
+                    {faceNames.map((faceName) => (
+                      <DropdownMenuItem
+                        key={faceName}
+                        className="flex cursor-pointer gap-2 capitalize"
+                        onClick={() => onTrainAttempt(faceName)}
+                      >
+                        <LuScanFace />
+                        {faceName}
+                      </DropdownMenuItem>
+                    ))}
+                  </DropdownMenuContent>
+                </DropdownMenu>
+                <TooltipContent>{t("trainFace")}</TooltipContent>
+              </Tooltip>
+              <Tooltip>
+                <TooltipTrigger>
+                  <LuRefreshCw
+                    className="size-5 cursor-pointer text-primary-variant hover:text-primary"
+                    onClick={() => onReprocess()}
+                  />
+                </TooltipTrigger>
+                <TooltipContent>{t("button.reprocessFace")}</TooltipContent>
+              </Tooltip>
             </div>
           </div>
-          <div className="flex flex-row items-start justify-end gap-5 md:gap-4">
-            <Tooltip>
-              <DropdownMenu>
-                <DropdownMenuTrigger asChild>
-                  <TooltipTrigger>
-                    <AddFaceIcon className="size-5 cursor-pointer text-primary-variant hover:text-primary" />
-                  </TooltipTrigger>
-                </DropdownMenuTrigger>
-                <DropdownMenuContent>
-                  <DropdownMenuLabel>{t("trainFaceAs")}</DropdownMenuLabel>
-                  {faceNames.map((faceName) => (
-                    <DropdownMenuItem
-                      key={faceName}
-                      className="cursor-pointer capitalize"
-                      onClick={() => onTrainAttempt(faceName)}
-                    >
-                      {faceName}
-                    </DropdownMenuItem>
-                  ))}
-                </DropdownMenuContent>
-              </DropdownMenu>
-              <TooltipContent>{t("trainFace")}</TooltipContent>
-            </Tooltip>
-            <Tooltip>
-              <TooltipTrigger>
-                <LuRefreshCw
-                  className="size-5 cursor-pointer text-primary-variant hover:text-primary"
-                  onClick={() => onReprocess()}
-                />
-              </TooltipTrigger>
-              <TooltipContent>{t("button.reprocessFace")}</TooltipContent>
-            </Tooltip>
-          </div>
         </div>
       </div>
-    </div>
+    </>
   );
 }
 
@@ -643,6 +772,8 @@ type FaceGridProps = {
   onDelete: (name: string, ids: string[]) => void;
 };
 function FaceGrid({ faceImages, pageToggle, onDelete }: FaceGridProps) {
+  const sortedFaces = useMemo(() => faceImages.sort().reverse(), [faceImages]);
+
   return (
     <div
       className={cn(
@@ -650,7 +781,7 @@ function FaceGrid({ faceImages, pageToggle, onDelete }: FaceGridProps) {
         isDesktop ? "flex flex-wrap" : "grid grid-cols-2",
       )}
     >
-      {faceImages.map((image: string) => (
+      {sortedFaces.map((image: string) => (
         <FaceImage
           key={image}
           name={pageToggle}

web/src/types/face.ts

@@ -3,6 +3,7 @@ export type FaceLibraryData = {
 };
 
 export type RecognizedFaceData = {
+  filename: string;
   timestamp: number;
   eventId: string;
   name: string;