Open-Set Cattle Muzzle Identification: A Leakage-Controlled Benchmark and Evaluation Protocol

Lalit BC; Dharmendra Singh Chaudhary; Shovit Nepal

cs.CVAug 22, 2026

Open-Set Cattle Muzzle Identification: A Leakage-Controlled Benchmark and Evaluation Protocol

Authors: Lalit BC, Dharmendra Singh Chaudhary, Shovit Nepal

Organizations: Fort Valley State University, School of Environmental, Civil, Agricultural and Mechanical Engineering, Fort Valley, GA, USA · Map Mentors, Department of Electronics and Computer Engineering, Lalitpur, Nepal · Tribhuvan University, Nepal · University of Georgia, USA

Abstract

Reliable individual cattle identification supports disease surveillance, vaccination records, breeding management, and livestock insurance. Although the bovine muzzle provides a stable, non-contact biometric, existing muzzle-recognition systems largely assume a closed set of enrolled animals, limiting their practical deployment. We reformulate cattle muzzle biometrics as an open-set, gallery-based identification problem that can reject previously unseen animals and support incremental enrollment without model retraining. We introduce a leakage-controlled evaluation protocol based on identity-disjoint splits, per-fold retraining, held-out threshold calibration, verified duplicate removal, and bootstrap confidence intervals. We evaluate the framework using two contrasting embedding configurations: a hybrid CNN-ViT metric-learning model and the MegaDescriptor-L foundation model. Under oracle threshold selection, the hybrid model achieves detection-and-identification rates of 98.3%, 96.4%, and 93.6% at target false-acceptance rates of 10^(-1), 10^(-2), and 10^(-3), respectively, while MegaDescriptor-L achieves 99.3%, 98.1%, and 96.1%. However, deployable threshold calibration reveals a substantial difference between oracle and calibrated performance: the hybrid model achieves a false-acceptance rate of 1.03% at a 1% target, whereas MegaDescriptor-L reaches 2.44%. Incremental enrollment further achieves Rank-1 accuracy above 91% with a single reference image and up to 97.3% with eight reference images, without retraining the model or degrading the existing gallery. These results demonstrate that threshold calibration, leakage control, and embedding quality are critical for reliable open-set cattle identification and provide a practical evaluation framework for deployment-oriented animal biometric systems.

Figures & tables

Figure 1: System pipeline overview: (top) muzzle image acquisition and preprocessing, (middle) hybrid CNN-ViT embedding model, (bottom) open-set identification with enrollment and threshold-based rejection.

Stage	Identities	Images
Original database [ 42 ]	268	4923
$-$ fewer than 4 images per identity	0	0
$-$ verified duplicate identities	19	280
Retained benchmark	249	4643

Table 1: Dataset provenance.

Figure 2: Hybrid CNN-ViT embedding architecture: (left) dual-branch feature extraction with cross-attention fusion, (right) gated pooling and quality weighting for robust embedding.

Figure 3: Leakage-controlled protocol with identity-disjoint splits: known (124), validation-unknown (50), and test-unknown (75) identities are partitioned separately to prevent information leakage and ensure valid evaluation at strict false-accept rates.

Metric	Hybrid CNN–ViT	MegaDescriptor-L
Rank-1 (%)	$99.53\;[99.03,99.86]$	$99.86\;[99.72,99.97]$
Verification EER (%)	$0.18$	$0.07$
Open-set EER (%)	$2.77\;[1.90,3.50]$	$1.63\;[1.02,2.06]$
DIR@ $10$ (%)	$98.31\;[97.46,99.01]$	$99.29\;[98.81,99.61]$
DIR@ $10$ (%)	$96.37\;[95.07,97.64]$	$98.10\;[97.26,98.92]$
DIR@ $10$ (%)	$93.59\;[92.18,95.79]$	$96.07\;[95.32,98.10]$

Table 2: Open-set identification, plain cosine, oracle threshold.

Figure 4: Open-set classification rate (OSCR) curve for hybrid CNN-ViT model: plot of true positive rate vs. false positive rate at multiple operating thresholds.

Figure 5: Detection-and-identification rate (DIR) curve for hybrid CNN-ViT model at target false-accept rates of

10

10

, and

10

Figure 6: Open-set classification rate (OSCR) curve for the MegaDescriptor-L foundation model: demonstrates consistent protocol validity across different embedding architectures.

Figure 7: Score distribution comparison: hybrid CNN-ViT model with known and unknown sample scores, illustrating the separation between enrolled and unenrolled animals.

Figure 8: Score distribution comparison: MegaDescriptor-L foundation model showing clean separation between known and unknown score distributions.

Score	$10$	$10$	$10$	OSCR-AUC
Plain cosine	99.29	98.10	96.07	0.9953
Entropy	99.29	98.10	96.04	0.9953
AS-norm	99.29	97.85	94.92	0.9953
Combined	99.21	97.31	93.69	0.9955
Margin	99.21	97.13	92.97	0.9954
OpenMax	91.96	90.32	89.23	0.9354

Table 3: Scoring-function ablation (foundation embedding; DIR@FAR %, five-fold mean).

Figure 9: Threshold transferability across folds: per-fold performance when threshold calibrated on val-unknown is transferred unchanged to test-unknown, showing deployment consistency.

	$n$	Rank-1 new	DIR@ $\tau$ new	Rank-1 orig.	Reject unk.
Hybrid	1	91.08	75.63	99.39	98.83
	3	95.94	86.22	99.39	98.76
	5	96.35	88.28	99.38	98.80
	8	97.07	89.37	99.38	98.84
Foundation	1	92.61	77.54	99.80	98.86
	3	96.46	86.38	99.77	98.86

Table 4: Incremental enrollment of previously unseen identities (no retraining).

n

: enrollment images per new identity.

Figure 10: Incremental enrollment demonstration: gallery-based enrollment with no retraining enables adding new identities while maintaining performance on the existing gallery.

Figure 11: t-SNE visualization of embedding space: hybrid CNN-ViT model showing known identities (colored clusters) and unknown samples (gray), demonstrating the separation necessary for open-set rejection.

Figure 12: Failure modes analysis: representative examples of misidentification and false rejections from the hybrid model, highlighting challenging cases with poor image quality, occlusion, or extreme poses.

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