Pith Number

pith:KPF2VFJR

pith:2026:KPF2VFJRIO3VCPREQEIPHS2OLD

not attested not anchored not stored refs resolved

MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment

Arun Kumar, Aswathy Baiju, Dmitry Ignatov, Radu Timofte

MobileAgeNet shows a compact network can estimate facial age accurately enough for mobile phones while keeping inference fast after format conversion.

arxiv:2604.17007 v1 · 2026-04-18 · cs.CV · cs.AI

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\pithnumber{KPF2VFJRIO3VCPREQEIPHS2OLD}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

MobileAgeNet achieves an MAE of 4.65 years on the UTKFace held-out test set while maintaining efficient on-device inference with an average latency of 14.4 ms measured using the AI Benchmark application.

C2weakest assumption

The assumption that the bounded age regression and two-stage fine-tuning strategy improve generalization and training stability on the specific dataset without introducing bias or limiting applicability to other face datasets or real-world conditions.

C3one line summary

MobileAgeNet uses a MobileNetV3-Large backbone with a regression head to achieve 4.65 years mean absolute error in age estimation and 14.4 ms on-device latency with 3.23 million parameters.

References

26 extracted · 26 resolved · 0 Pith anchors

[1] https://susanqq.github.io/UTKFace/ 2026

[2] Age estimation via face images: a survey.EURASIP Journal on Image and Video Processing, 2018(1):42, 2018 2018

[3] DAA: A delta age adain operation for age estimation via binary code transformer 2023

[4] Using ranking-CNN for age estimation 2017

[5] Imagenet: A large-scale hierarchical image database 2009

Receipt and verification

First computed	2026-06-02T02:04:17.775943Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

53cbaa953143b7513e248110f3cb4e58eb633b128423cceff2b7c6325535dcff

Aliases

arxiv: 2604.17007 · arxiv_version: 2604.17007v1 · doi: 10.48550/arxiv.2604.17007 · pith_short_12: KPF2VFJRIO3V · pith_short_16: KPF2VFJRIO3VCPRE · pith_short_8: KPF2VFJR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KPF2VFJRIO3VCPREQEIPHS2OLD \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 53cbaa953143b7513e248110f3cb4e58eb633b128423cceff2b7c6325535dcff

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4fedaa4ac2621790a6675791ac8127495562b1a7e27c00e99783e9513a93e0fa",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/publicdomain/zero/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-18T14:37:23Z",
    "title_canon_sha256": "9cda30c621ebf771eda757d83e5b2570938a58ef27eaee71e4a12f38e683a4a5"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.17007",
    "kind": "arxiv",
    "version": 1
  }
}