Drop up to 20 photos — or the .MOV part of a Live Photo — and get a ranked winner based on face framing, angle, sharpness, light, and expression. Videos are broken down frame by frame so a blink or blur doesn’t ruin the shot. Then, after you pick one, add only a slight chisel to the jaw and take the grey out of your beard — no generative AI, and nothing else changes.
Tip for iPhone Live Photos: In Photos, open a Live Photo → Share → Save as Video, or export both files from iCloud.com / Mac. Upload the still + its .MOV here together.
Research check: expression and light beat fine angle tweaks — in the largest crowd-rating datasets, a teeth-visible smile moved scores far more than any camera angle did. This scorer weights them accordingly. Full evidence in the Angle Guide below. Change “Prioritize” to re-rank your way.
JPG, PNG, WEBP, HEIC previews where your browser supports them, plus MP4 / MOV for Live Photo motion. Everything stays on this device.
Touch-up is optional and only runs after you choose a photo. For a video / Live Photo winner, it works on the best frame and download grabs that frame as a JPG still.
Start from the photo you picked. Every edit is a local, deterministic slider — jaw shape, beard colour, and slight angle tweaks. Defaults stay subtle or off; how strong it gets is entirely up to you. Check everything with the big Before / After slider below.
Opt-in only. Darkens just the grey / white hairs in the beard — below the mouth, along the jaw and chin — back toward your darker beard colour nearby. At high strength it now blends grey almost fully back (up to ~95% at 100, previously capped at ~62%), while each hair keeps its own light and shade so the texture stays — never a flat dye. Skin, lips, teeth, head hair and clothes are outside the feathered beard mask, so they don’t change.
All optional, all default to off. These are honest 2D tools — a rotation and two small warps — not a true 3D re-pose of your head. Within these caps they read as the same photo taken a touch differently.
Defaults are set low on purpose. If you can’t quite tell at first glance, it’s at the right strength — drag the Before / After slider or hit Reset to check.
One tap lifts you out of the photo you chose (with whatever touch-ups are dialled in above) and sets you into five new scenes. Honest label: these are background replacements — every pixel of the person is your original photo; only the background is new, drawn right here in the app. No generative AI, no face or body changes, nothing leaves your device. Research note (Angle Guide, rule 11): in controlled ratings the backdrop itself showed no significant effect — what these five fix is a cluttered, dark or bathroom/car background, which is exactly what does score down.
Feather softens the cut-out edge — if a hairline or shoulder looks rough, raise it a little and the set rebuilds. If the cut-out itself looks wrong on your photo, the note below will say so; you can still keep and download any of the five.
Uses your browser’s FaceDetector when available, plus centering, face size, symmetry and skin-region checks as a fallback. Near-frontal, centered, neither too close nor too tiny scores highest — matching the research band in the Angle Guide below: eye level to ~15° above, lead photo within ~15° of frontal. Distance matters more than degrees: at ~30 cm a modelled nose reads ~30% wider, so step back.
Measures real pixels: exposure (not blown out, not murky), contrast, color vibrance, and Laplacian sharpness for blur / motion shake. Research agrees light is a top factor — direct flash aged faces ~7 years in OkCupid’s 11.4M-opinion EXIF data, and photos taken just after sunrise / before sunset rated best. That’s why a burst frame 0.3s later can outrank the cover still.
For .MOV / MP4, the analyzer samples frames across the whole clip, scores each one, and surfaces the best still — perfect for catching the moment before a blink or after a smile settles.
Only after you choose a photo: a jaw chisel that now goes up to a strong ~11% of face width if you dial it there, grey removal in the beard up to near-full, plus slight angle tools — level a head tilt, sharpen the jaw angle, or nudge the perspective a touch. All local 2D warps and colour edits. No generative AI, no face regeneration.
We dug through large-sample crowd ratings (Photofeeler, OkCupid, Hinge reporting), peer-reviewed camera-angle studies, photographer technique, and community consensus (Reddit review culture, Photofeeler reviewer panels, looksmaxxing “anglemaxxing” threads). No study finds a magic angle like “17° above, 32° turned.” Labs only ever tested 0° vs ±30° — every finer degree band below is photographer/community convention, and it’s labelled that way. What the evidence does converge on: expression beats angle, distance is the most quantified factor, and light can make or break the jaw. Evidence grades: A = large-sample crowd/platform data (10k+ ratings) · B = peer-reviewed study · C = community consensus · D = creator/photographer opinion.
Trust peaks at exactly eye level (Baranowski & Hecht 2018: 5.03/9 at eye level vs 4.57 above, 4.27 below — and elevation alone did not change attractiveness). A raise only helped attractiveness combined with a 30° side turn (Schneider & Carbon 2017). Men-specific warning (Makhanova et al. 2017, Psychological Science): from-below made men look more dominant — mostly to other men — not more attractive. Too high (≳30°): forehead enlarges, jaw recedes, you read younger/smaller. Too low: up-the-nose, under-chin emphasis, face judged heavier. The “hold it 10–15° above” figure itself is photographer/community convention, not a measured optimum.
Schneider & Carbon 2017 · Makhanova et al. 2017 summary · Photofeeler angles course
A modest turn beat dead-frontal for attractiveness in lab ratings (tested at 30°; effect stronger for female faces). There is no winning side: the direct left-vs-right comparison was null. The famous “left cheek” finding (Lindell 2017: 41% of 2,000 Instagram selfies showed the left cheek vs 31.5% right) is a posing preference — people choose their left — not evidence raters score it higher. So: don’t chase a side; test both and keep the one you consistently prefer.
Schneider & Carbon 2017 · Lindell 2017 (via Frontiers editorial)
Ward et al. 2018 modelled it: at 12 in (30 cm) the nasal base appears +30% wider in men (+29% women), tip +7%; at 5 ft, essentially no distortion. Caveat: that’s a geometric model, not photos of real people. The rating cost is real, though — Bryan, Perona & Adolphs 2012 (Caltech) photographed the same faces close (0.45 m) vs far (1.35 m) and close faces got lower trust, competence and attractiveness ratings, and viewers couldn’t say why. It’s the distance that distorts, not the lens — a longer lens only helps because it forces you back.
This is Peter Hurley’s core move: “Jam your forehead towards the camera and allow the chin to follow, tightening the skin against your jawline.” It’s unanimous photographer technique (grade D), and the extremes are study-backed: pitching the face up raises perceived dominance but lowers attractiveness (Marshall et al. 2020), and a bowed head reads submissive/sad. Forward-then-slightly-down is specifically not a bow — the extended neck is what stops an under-chin fold forming.
Photofeeler’s 60,000+-rating study: teeth-visible smile = +1.35 likability — “by far the most impactful characteristic we found” — plus +0.33 competence; squinch added +0.33/+0.22/+0.37. Direct gaze is the trust winner in lab work, and direct gaze + smile is the classic attractiveness combo. Averted gaze is fine for candid secondary shots (downward-averted reads best of those; sideways/upward worst). Sunglasses in the lead photo: −0.36 likability — eyes must be visible.
Photofeeler “Perfect Profile Photo” (2014) · Photofeeler science of smiles (2026)
Direct flash is the villain (OkCupid EXIF: ≈ +7 years; most flash photos in the data were direct, not bounced). Overhead indoor downlights — the bathroom/office look — shadow the eyes, nose and chin and age the face; too-dark photos cost −0.38 likability (Photofeeler). Overcast sky is a free softbox. Ring lights are a divided creator opinion with no rating data: acceptable fallback in a dark room at eye level or slightly above, but window/golden-hour light beats them — and reads more natural.
OkCupid “Don’t Be Ugly By Accident” (archive) · Photofeeler 2014
The measurable part is the shadow line: a shadow outlining the jaw all the way around scored Competence +0.24, Likability +0.18, Influence +0.18 in Photofeeler’s 60k-rating study. Jaw definition is a lighting outcome as much as an anatomy one — taut skin (neck extended) is what gives light an edge to cut. Biting your back teeth together for the shot is a real photographer trick to flex the jaw muscle for one instant — no study behind it, and habitual clenching risks jaw strain, so treat it as a 1-second pose at most.
The honest catch: in Photofeeler’s controlled business study, the setting itself — studio vs nature vs urban vs home — had no significant effect at all. What dating data rewards is the activity/social signal, not the backdrop: Hinge reporting (secondary, no primary report retrievable) put sports at +75% likes, night-out/social +74%, candid +15% vs posed. Framing is solid A-grade: face-only close-ups −0.21 likability; full-body as a lead −0.29 competence/influence (a full-body shot still belongs later in a set). Shallow depth of field rated best in OkCupid’s EXIF data. Heavy filters: Hinge reporting put Snapchat-filter photos at −90%.
Photofeeler 2014 · Hinge 2017 via Bustle · Photofeeler reviewers on men’s photos
Sources (researched Nov 11, 2026): Photofeeler 2014 — 60,000+ ratings · Photofeeler 2026 — science of smiles · Photofeeler 2017 replication (n=7,140) · OkCupid — 11.4M opinions (archive) · Schneider & Carbon 2017 · Ward et al. 2018 (nose distortion) · Bryan et al. 2012 (close vs far) · Baranowski & Hecht 2018 (eye-level trust) · Hinge 2017 via Bustle (secondary) · Photofeeler — women review men’s photos